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>> WELCOME, EVERYBODY.

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IT'S VERY GOOD TO HAVE YOU HERE.

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THIS IS THE 30th MEETING OF

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THE BRAIN MULTICOUNCIL WORKING

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GROUP.

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I AM SUSAN WEISS, I'M THE

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DIRECTOR OF THE DIVISION OF

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EXTRAMURAL RESEARCH AT THE

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NATIONAL INNSITUTE ON DRUG

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ABUSE.

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AND I AM HERE TODAY SERVING AS

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THE DESIGNATED FEDERAL OFFICIAL

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-- NOT FOREIGN -- FEDERAL

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OFFICIAL FOR THE BRAIN

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MULTICOUNCIL WORKING GROUP.

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AS MANY OF YOU ALREADY KNOW THE

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MCWG IS NOT A NORMAL ADVISORY

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COMMITTEE UNDER THE FEDERAL

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ADVISORY COMMITTEE ACT OR FACA,

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IT SERVES IN AN ADVISORY ROLE TO

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THE 10 ADVISORY COUNCILS THAT

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ARE PART OF THE BRAIN INITIATIVE

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INSTITUTES AND  CENTERS EMPLOY T

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WAS ESTABLISHED BECAUSE OF THE

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RECOGNIZED NEEDS OF VARIOUS

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EXPERT EEDZ IN VARIOUS

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DISPOLICEINS IN ORDER TO

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PROPERLY OVERSEE RESEARCH

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SUPPORTED THROUGH BRAIN.

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SOME OF THE MEMBERS ARE FROM THE

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IC ADVISORY COUNCILS AND HAVE

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ALSO ADDED LARGE MEMBERS TO

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SUPPLEMENT THE GROUP'S WORKING

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EXPERTISE, WE ALSO HAVE SEVERAL

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EXOFICI IO PARTNERS, INVOLVED.

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WE DEPEND UPON THE MCWG FOR

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ONGOING OVERSIGHT OF THE

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SCIENTIFIC VISION AND PROGRESS

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OF THE BRAIN INITIATIVE FOR

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FEEDBACK ON CONCEPT CLEARANCES

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FOR NEW RFAs, AND FOR INPUT ON

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OUR FUNDING PLANS DURING THE

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CLOSED SESSION.

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THE OPEN SESSION WHICH WE ARE IN

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NOW, IS BEING VIDEOCAST AND

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RECORDED AND WILL BE ARCHIVED

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AND POSTED ON THE NIH AND BRAIN

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WEBSITES.

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TODAY, WE ARE PLEASED TO WELCOME

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SEVERAL NEW MEMBERS TO THE MCWG

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-- MCWG, DRS. EDWARD CHANG HAD

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WHO IS AN AT LARGE MEMBER,

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LESHON LO, AND TODD CONSTABLE

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FROM NIBIB.

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AND WE ARE BIDDING FAREWELL TO 3

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OTHER MEMBERS, DRS. TED ABLE

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FROM NIMH, TARYN MOORE FROM NEI

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AND BRIAN ROTH FROM THE NIH

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COUNCILS.

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JOHN IS GOING TO FORMALLY

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WELCOME OUR NEW MEMBERS AND

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ACKNOWLEDGE THE CONTRIBUTIONS OF

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THOSE WHO ARE ROUGH ATOM

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PARTICIPATE  ROTATION

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OFFICEITATING OFF IN JUST A FEW

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MINUTES DURING HIS GENERAL

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UPDATE.

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AFTER THAT WE ARE LUCKY ENOUGH

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TO HAVE A PRESENTATION BY

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DR. MALA MURTHY, ON THE FLY

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PROGRAM WHERE SHE MAPPED THE

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ENTIRE BRAIN OF DROSOPHILA AND

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THIS IS QUITE AN ACCOMPLISHMENT

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AND WE ARE LUCKY ENOUGH TO HEAR

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MORE ABOUT THIS LATER TODAY.

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FOLLOWING THE OPENING SESSION,

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WE WILL HAVE A BREAK AND DURING

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THE CLOSED SESSION I WILL GO

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OVER A FEW OPERATING PROCEDURES.

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WE EXPECT THAT TO START AROUND

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2:30 P.M.  THERE ARE DIFFERENT

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TEAMS LINKS FOR THE OPEN AND

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CLOSED SESSION.

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ALSO AS YOU KNOW, AS YOU

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NOTICED, WE ARE USING TEAMS AND

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NOT ZOOM, SO WE ARE HOPING THAT

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EVERYTHING WILL RUN SMOOTHLY BUT

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THIS IS NEW TO MOST OF US FOR

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THIS KIND OF A MEETING SO PLEASE

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BEAR WITH US.

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FEW HOUSEKEEPING ITEMS, WE ASK

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THAT YOU MUTE YOUR LINE DURING

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PRESENTATIONS OR DURING -- OR

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WHEN OTHER PEOPLE ARE SPEAKING

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DURING THE DISCUSSION, WE WILL

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LIKE ON YOU TO PUT YOUR VIDEO ON

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AND USE THE RAISES HAND OPTION

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IF WE HAVE QUESTIONS AND THEN WE

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WILL CALL ON YOU TO UNMUTE

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YOURSELF AND AGAIN, BECAUSE THIS

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IS AN OPEN SESSION, IT'S BEING

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VIDEOCAST, WE PREFER YOU NOT TO

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USE THE CHAT FUNCTION FOR

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DISCUSSIONS SINCE THAT WOULD NOT

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BE VISIBLE THROUGH THE

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VIDEOCAST.

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FINALLY, I WANT TO THANK THE

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OUTSTANDING TEAM WHOSE HARD WORK

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WENT INTO PREPARING FOR THIS

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MEETING, IN PARTICULAR

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DR. CRYSTAL LANCE WHO HAS TAKEN

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OVER FOR DR. KRISTIN DUPREE, WHO

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IS UNFORTUNATELY IS NO LONGER

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WITH THE INSTITUTE.

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DR. NINA, SHOO, MISS DEBFREENER

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AND DEBPEARSON WHO ARE

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OUTSTANDING COLLEAGUES WHO DO A

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WHOLE LOT OF WORK BEHIND THE

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SCENES TO MAKE THIS HAPPEN AND

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THEY ARE BOTH EXTREMELY

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EFFECTIVE AND VERY PLEASANT TO

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WORK WITH AS I'M SURE YOU KNOW

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IF YOU ALREADY WORKED WITH THEM.

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AT THIS POINT, I AM GOING TO

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TURN IT OVER TO JOHN.

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>> THANKS, SUSAN, THANK
YOU FOR

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THAT, GREAT TO SEE EVERYBODY

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HERE AND BIG THANKS TO THE FOLKS

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THAT MADE THIS HAPPEN, MICHELLE,

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CRYSTAL, NINNA AND DEB.

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COULD NOT DO THIS WITHOUT YOU

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AND OF COURSE WE MISS KRISTIN

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DUPREE, BUT SHE HELPED US GET TO

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WHERE WE ARE, WORKING WITH THE

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THING THROUGH TEAMS, THE

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TRANSITION, I THINK WE SRO IT

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WORKED OUT BUT WE'VE DONE MANY,

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MANY DRY RUNS BUT IF WE RUN INTO

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GLITCHES, JUST HAVE PATIENCE, WE

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WILL GET THROUGH T. SO I WILL

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LIKE TO GIVE UPDATES, WHICH IS

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BECAUSE OF VARIOUS, ISS, WAS A

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WHILE AGO, WE MISSED OUR JANUARY

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MEETING AS YOU KNOW, BUT I WOULD

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LIKE TO SHARE WITH YOU THE

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THINGS WE AND YOU ALL HAVE BEEN

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UP TO AND THEN SUSAN MENTIONED

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WE HAVE A REAL TREAT, TALK FROM

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OUR OWN MEMBER MALA MURTHY, FROM

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BRAIN WIRING TO BEHAVIOR IN

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DROSOPHILA, THAT'S A REAL TREAT

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COMES UP FOR US, SO LET'S SEE,

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CAN I DO THIS?

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OKAY, CAN YOU SEE SOME OF WHAT

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IT LOOKS LIKE WE SHOULD BE

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SHOWING YOU?

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ARE WE GOOD?

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I CAN'T SEE ANYBODY, I'M

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ASSUMING WE'RE GOOD.

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SUSAN ARE WE GOOD?

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JUST BLINK ONCE.

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>> WE'RE GOOD, WE'RE GOOD.

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SORRY!

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>> OKAY.

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SO HERE'S MY UPDATE FOR YOU

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TODAY ON MAY EIGHTH.

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AS SUSAN MENTIONED WE HAVE 3

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MEMBERS CYCLING, OFF TARYN

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REPRESENTING NIH, TARA,

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REPRESENTING NEI AND BRIAN

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REPRESENTING NG RA, WE HAVE BEEN

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APPRECIATIVE TO HAVE YOU WITH

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US, APPRECIATE YOUR INPUT, GOOD

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HUMOR AND GRACE IN HELPING US

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MAKE THIS PROGRAM WHAT IT IS.

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AND THEN SUSAN ALSO MENTIONED WE

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HAVE A FEW MEMBERS COMING ON,

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DELIGHTED TO WELCOME EDDIE CHANG

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TO THE GROUP, I DON'T KNOW IF

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HE'S HERE BUT IN ANY CASE, CHAIR

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OF SURGERY AT UCSF, HE DIRECTS

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THE JOINT UCSF, UC BERKELEY FOR

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SHARING AND PROSTHESIS, HE'S A

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NEUROSURGEON WHO SECIALIZES IN

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THE TREATMENT OF EPILEPSYS,

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GENERAL NEUROLOGY AND BRAIN

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TUMORS AND I THINK YOU'VE SEEN

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SOME OF HIS BRILLIANT WORK ON

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BRAIN MACHINE INTERFACES AND

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ALSO IN OTHER ASPECTS USING

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INVASIVE NEURAL PROCEDURES.

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SO IT'S REALLY A DELIGHT TO HAVE

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EDDIE ON BOARD WITH US WITH THE

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BRAIN INITIATIVE.

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ALSO DELIGHTED TO WELCOME A GOOD

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FRIEND AND BOLD CLEGLY CHAN LO

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FROM STANFORD UNIVERSITY.

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LEE CHAN IS THE ANN AND BILL

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SWINDELL'S PROFESSOR IN THE

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SCHOOL OF HUMANITIES AND

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SCIENCES, PROFESSOR OF BIOLOGY,

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NEUROBIOLOGY AT STANFORD.

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HE'S BEEN A HOWARD HUGHES

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MEDICAL INVESTIGATOR FOR MANY,

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MANY YEARS, HE'S PIONEERED FROM

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THE POST DOC WITH GEN COLLAB,

207
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HE'S PIONEERED TOOLS FOR

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DISSECTING NEURAL CIRCUITS IN

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MAMMALS AND IT'S LED TO CIRCUIT

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BASES OF BEHAVIOR AS WELL AS IN

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DEVELOPMENT SO THIS IS A GREAT

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EMBODIMENT OF HOW TECHNOLOGY CAN

213
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REALLY ACCELERATE DISCOVERY AND

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SOME OF YOU MAY KNOW, LIQUN AS

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THE PRINCIPALS OF NEUROBIOLOGY,

216
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IT'S A GREAT TEXTBOOK FOR

217
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UNDERGRADUATES AND BEYOND AS

218
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WELL AS GRAD STUDENTS AND I

219
00:07:44,338 --> 00:07:45,439
REMEMBER WHEN HE FIRST PUBLISHED

220
00:07:45,439 --> 00:07:47,241
THIS BOOK, IT WAS A BIT OF A

221
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QUANDARY WHEN I WAS TEACHING

222
00:07:50,211 --> 00:07:50,778
INTRA DUCTORY NEUROBIOLOGY

223
00:07:50,778 --> 00:07:52,280
BECAUSE I HAD TO REFORM SOME OF

224
00:07:52,280 --> 00:07:54,115
MY LECTURES BECAUSE IT WAS JUST

225
00:07:54,115 --> 00:07:55,549
A BETTER BOOK THAN WHAT WE WERE

226
00:07:55,549 --> 00:07:56,851
USING BEFORE BUT IT WAS A GREAT

227
00:07:56,851 --> 00:07:58,919
OPPORTUNITY FOR ALL OF US TO UP

228
00:07:58,919 --> 00:08:03,257
OUR TEACHING GRAIM AND LIQUN,

229
00:08:03,257 --> 00:08:04,258
REALLY APPRECIATE YOU FOR THAT.

230
00:08:04,258 --> 00:08:06,294
I THINK YOU'RE ON THE SECOND OR

231
00:08:06,294 --> 00:08:09,263
THIRD EDITION IF I'M NOT

232
00:08:09,263 --> 00:08:13,701
MISTAKEN AND THEN DR. RSOTO, AND

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IMMUNOLOGY AND NEUROSCIENCE AT

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BAYLOR COLLEGE OF MEDICINE,

235
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DIRECTS THE EDUCATION DIRECTOR

236
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OF EDUCATION OF THE DUNCAN

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NEUROLOGICAL RESEARCH INSTITUTE,

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COMMITMENT AND GRADUATE

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DEVELOPMENT WHICH WHICH WE ALL

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APPRECIATE AND THIS IS REFLECTED

241
00:08:31,085 --> 00:08:34,121
IN RECEIVING THE 2021 NINDS

242
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AWARD FOR OUTSTANDING MENTORSHIP

243
00:08:36,691 --> 00:08:46,367
AND HIS RESEARCH IS MOUSE MODELS

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AND NOT TO MENTION THE

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CEREBELLUM DISEASE AND OTHER

246
00:08:50,438 --> 00:08:52,907
RELATED DISORDERS AND THEN OUR

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00:08:52,907 --> 00:09:03,517
NEW MEMBER ISED  TODD CONSTABLEO

248
00:09:06,287 --> 00:09:07,855
IS AT YALE UNIVERSITY, CO

249
00:09:07,855 --> 00:09:10,224
DIRECTS MRI RESEARCH CENTER AND

250
00:09:10,224 --> 00:09:13,361
HIS RESEARCH FOCUS ON USES

251
00:09:13,361 --> 00:09:14,562
FUNCTIONAL MAG NEDRAIC IMAGING

252
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TO MAP THE BRAIN AND TO

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UNDERSTAND THE RELATIONSHIP

254
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BETWEEN THIS ORGANIZATION AND

255
00:09:18,933 --> 00:09:22,436
BEHAVIOR, AND IS REALLY BEEN

256
00:09:22,436 --> 00:09:23,504
FOCUSING ON ENGINEERING EXPOSURE

257
00:09:23,504 --> 00:09:24,271
TO RADIATIONS TO DEVELOP

258
00:09:24,271 --> 00:09:27,308
NOVARTIS WILL AMERICAS RI

259
00:09:27,308 --> 00:09:28,542
DEVICES INCLUDING POINT OF FIELD

260
00:09:28,542 --> 00:09:30,177
MRIs AND THE POETIC TEBTIAL TO

261
00:09:30,177 --> 00:09:35,049
MAKE MRI MORE ACCESSIBLE AND

262
00:09:35,049 --> 00:09:36,984
THIS IS 1 OF OUR ETHICS STUDIES

263
00:09:36,984 --> 00:09:38,953
AND THIS IS JUST OUR GROUP OF

264
00:09:38,953 --> 00:09:41,789
MEMBERS AND I THANK YOU ALL FOR

265
00:09:41,789 --> 00:09:43,057
YOUR DEDICATION, YOUR

266
00:09:43,057 --> 00:09:46,026
WILLINGNESS TO TICK WITH US AND

267
00:09:46,026 --> 00:09:47,128
TO BE TOLERANT OF MY QUESTIONS

268
00:09:47,128 --> 00:09:48,963
AND ASKS AND I THINK IT'S ALL

269
00:09:48,963 --> 00:09:53,367
HELP MAKE THE INITIATIVE WHAT IT

270
00:09:53,367 --> 00:09:55,302
IS TODAY NOW NOTHING WOULD

271
00:09:55,302 --> 00:09:56,404
HAPPEN WITHOUT OUR INCREDIBLE

272
00:09:56,404 --> 00:09:58,439
STAFF WITHIN NIH AND WE ORGANIZE

273
00:09:58,439 --> 00:10:00,841
THE STAFF INTO TEAMS AND THIS IS

274
00:10:00,841 --> 00:10:02,476
OUR GROUP AND I WANT TO POINT

275
00:10:02,476 --> 00:10:05,179
OUT WE HAVE 2 TEAM COLLEAGUES

276
00:10:05,179 --> 00:10:08,883
WHO NOW ON LEADS THE TEAM

277
00:10:08,883 --> 00:10:11,685
E-GROUP THAT OVERSEES INTEGRATED

278
00:10:11,685 --> 00:10:17,491
APPROACHES AND DR. JOE

279
00:10:17,491 --> 00:10:17,992
 MONICO

280
00:10:17,992 --> 00:10:20,728
WHO HELPS MAKE THIS HAPPENS,

281
00:10:20,728 --> 00:10:21,929
THERE'S WELL OVER 130 FOLKS, I

282
00:10:21,929 --> 00:10:23,330
WILL JUST TELL YOU RIGHT NOW,

283
00:10:23,330 --> 00:10:25,866
THIS SLIDE WAS MADE BACK IN

284
00:10:25,866 --> 00:10:26,400
JANUARY OF 2025.

285
00:10:26,400 --> 00:10:28,702
MANY OF THESE FOLKS ARE NOT WITH

286
00:10:28,702 --> 00:10:30,004
US BUT THEIR EFFORTS AND THEIR

287
00:10:30,004 --> 00:10:31,872
IMPACT ARE TILL WITH US AND I

288
00:10:31,872 --> 00:10:34,675
JUST WANT TOK KNOWLEDGE ALL

289
00:10:34,675 --> 00:10:35,409
THEIR GREAT CONTRIBUTIONS AND

290
00:10:35,409 --> 00:10:37,378
THOSE OF US WHO ARE LEFT ARE

291
00:10:37,378 --> 00:10:40,147
WORKING HARD TO MAKE SURE ALL

292
00:10:40,147 --> 00:10:44,485
THIS WORK CAN STILL HAPPEN.

293
00:10:44,485 --> 00:10:46,954
SO NOW, REALLY GREAT NEWS, WE

294
00:10:46,954 --> 00:10:49,023
HAVE NICE RECOGNITION OF PEOPLE

295
00:10:49,023 --> 00:10:51,292
IN THE BRAIN UNIVERSE AS IT

296
00:10:51,292 --> 00:10:55,062
WERE, THE INSTITUTE FOR

297
00:10:55,062 --> 00:10:58,065
NEUROLOGICAL AND IMAGING, ANIB,

298
00:10:58,065 --> 00:10:59,200
ELECTORRED THEIR INDUCTEES IN

299
00:10:59,200 --> 00:11:01,836
LATE FEBRUARY, AMONG THEM IS OUR

300
00:11:01,836 --> 00:11:04,004
OWN DR. GRACE WONG WHO IS A

301
00:11:04,004 --> 00:11:07,942
PROGRAM OFFICER AT NINDS AND SHE

302
00:11:07,942 --> 00:11:11,278
CARRIES A LOT OF THE BRAIN FORT

303
00:11:11,278 --> 00:11:14,682
FORT FOLIO AS WELL TO 2 OTHER

304
00:11:14,682 --> 00:11:19,787
INVESTIGATIONS DR. CHRIS ROZELL,

305
00:11:19,787 --> 00:11:23,190
AND DR. MARYAMSPAY SHANECHI, AND

306
00:11:23,190 --> 00:11:26,093
DELIGHTED TO SAY THAT ALL THESE

307
00:11:26,093 --> 00:11:28,028
FOLK VS COME ALONG IN THEIR

308
00:11:28,028 --> 00:11:29,363
SCIENCE WITH THE BRAIN

309
00:11:29,363 --> 00:11:30,631
INITIATIVE AND IT'S GREAT TO SEE

310
00:11:30,631 --> 00:11:33,400
THE RECOGNITION OF THEIR AMAZING

311
00:11:33,400 --> 00:11:33,868
ACCOMPLISHMENTS AND

312
00:11:33,868 --> 00:11:35,703
CONTRIBUTIONS AND THEN WOB OF

313
00:11:35,703 --> 00:11:40,207
OUR VERY OWN TONYA PASTER KNACK

314
00:11:40,207 --> 00:11:43,210
WAS JUST AWARDED VISION SCIENCES

315
00:11:43,210 --> 00:11:44,211
ASSOCIATE LIFETIME ACHIEVEMENT

316
00:11:44,211 --> 00:11:46,380
AWARD TO HER CONTRIBUTIONS TO

317
00:11:46,380 --> 00:11:48,716
VISION SCIENCE OVER HERE CAREER,

318
00:11:48,716 --> 00:11:50,584
RECOGNIZING HER COMPELLING

319
00:11:50,584 --> 00:11:51,285
RESEARCH ACHIEVEMENTS,

320
00:11:51,285 --> 00:11:52,553
INTELLECTUAL LEADERSHIP AND

321
00:11:52,553 --> 00:11:53,087
DEDICATED SERVICE TO THE

322
00:11:53,087 --> 00:11:54,054
SCIENTIFIC COMMUNITY AND I THINK

323
00:11:54,054 --> 00:11:57,024
YOU ALL SEE THAT IN THE SCIENCE

324
00:11:57,024 --> 00:12:04,999
WE SUPPORT, TONYA IS A SENIOR

325
00:12:04,999 --> 00:12:05,699
GRANTS OFFICER OVERSEEING REVIEW

326
00:12:05,699 --> 00:12:07,902
AND I THINK YOU CAN -- I HOPE

327
00:12:07,902 --> 00:12:11,071
YOU ALL AGREE THAT WE ARE

328
00:12:11,071 --> 00:12:12,439
FINDING GREAT SCIENCE IN NO

329
00:12:12,439 --> 00:12:19,046
SMALL PART BECAUSE OF TANIA'S

330
00:12:19,046 --> 00:12:19,513
CONTRIBUTION IN BRAIN.

331
00:12:19,513 --> 00:12:21,348
AND THEN FINALLY I'M DELIGHTED

332
00:12:21,348 --> 00:12:23,751
TO SAY OUR OWN ANDREW MITCHENER

333
00:12:23,751 --> 00:12:25,152
DEPUTY DIRECTOR OF BRAIN HAS

334
00:12:25,152 --> 00:12:28,722
BEEN RECEIPTLY BEEN NAMED ACTING

335
00:12:28,722 --> 00:12:30,858
DREAMTOR OF NATIONAL INTUITYS OF

336
00:12:30,858 --> 00:12:32,760
MENTAL HEALTH 1 OF OUR MAJOR

337
00:12:32,760 --> 00:12:34,628
FUNDERS IN THIS EFFORT.

338
00:12:34,628 --> 00:12:36,497
ANDREA HAS BEEN WITH THE NIH

339
00:12:36,497 --> 00:12:38,832
SINCE 2004 AND SHE WAS WITH THE

340
00:12:38,832 --> 00:12:40,668
BRAIN INITIATIVE SINCE ITS

341
00:12:40,668 --> 00:12:42,202
INCEPTION.

342
00:12:42,202 --> 00:12:43,304
IN FACT, ANDREA WAS THE PROGRAM

343
00:12:43,304 --> 00:12:46,607
OFFICER FOR THE INITIAL ROUND IN

344
00:12:46,607 --> 00:12:49,476
THE BRAIN CELL CENSUS PROGRAM,

345
00:12:49,476 --> 00:12:51,979
THE BRAIN INITIATIVE CELL CENSUS

346
00:12:51,979 --> 00:12:53,247
CONSORTIUM AND SHE WAS MY

347
00:12:53,247 --> 00:12:55,382
PROGRAM OFFICER SO I'M DELIGHTED

348
00:12:55,382 --> 00:12:56,550
ALL THIS COME FIRST CIRCLE

349
00:12:56,550 --> 00:13:00,287
HOPEFULLY WE WILL HAVE A

350
00:13:00,287 --> 00:13:01,221
PERMANENT NIMH DIRECTOR

351
00:13:01,221 --> 00:13:03,290
INSTALLED ONCE THE RESEARCH GETS

352
00:13:03,290 --> 00:13:11,298
UNDERWAY IN THE MEAN TIME NIMH

353
00:13:11,298 --> 00:13:13,734
IS DRIVING AND NIH THRIVING AND

354
00:13:13,734 --> 00:13:14,935
WE APPRECIATE HER LEADERSHIP AND

355
00:13:14,935 --> 00:13:16,503
STEPPING INTO THE NEW ROLE AS WE

356
00:13:16,503 --> 00:13:20,140
GO THROUGH THESE TRANSITIONS.

357
00:13:20,140 --> 00:13:23,043
AND THEN FINALLY, RECENTLY THE

358
00:13:23,043 --> 00:13:24,812
NATIONAL ACADEMY OF SCIENCES HAS

359
00:13:24,812 --> 00:13:26,313
ELECTED NEW MEMBERS AND THIS IS

360
00:13:26,313 --> 00:13:27,247
IN RECOGNITION FOR GREAT

361
00:13:27,247 --> 00:13:29,283
CONTRIBUTIONS TO SCIENCE AND

362
00:13:29,283 --> 00:13:35,189
AMONG THESE FOLKS WAS 3 BRAIN

363
00:13:35,189 --> 00:13:40,394
FUNDED INVESTIGATORS, DR. PAULO

364
00:13:40,394 --> 00:13:43,063
ARLOTTA, WHO IS BEEN A PIONEER

365
00:13:43,063 --> 00:13:46,033
AT LOOKING AT WAYS OF BRAIN

366
00:13:46,033 --> 00:13:46,900
DEVELOPMENT INCLUDING THROUGH

367
00:13:46,900 --> 00:13:49,036
ORGANOIDS WHICH IS REALLY A HOT

368
00:13:49,036 --> 00:13:49,403
AREA.

369
00:13:49,403 --> 00:13:51,839
EDDIE CHANG I JUST MENTIONED TO

370
00:13:51,839 --> 00:13:54,808
YOU FROM UCSF AND ADRIAN HALL AT

371
00:13:54,808 --> 00:13:57,645
WASHINGTON UNIVERSITY FOR HER

372
00:13:57,645 --> 00:13:58,779
CONTRIBUTIONS TOWARD APPROACHES

373
00:13:58,779 --> 00:14:00,447
TO BEDDING SYSTEMS LEVEL

374
00:14:00,447 --> 00:14:00,748
APPROACHES.

375
00:14:00,748 --> 00:14:01,949
SO VERY, VERY THRIFULLED THAT

376
00:14:01,949 --> 00:14:03,017
OUR INVESTIGATORS WE'VE BEEN

377
00:14:03,017 --> 00:14:05,219
SUPPORTED HAVE BEEN RECOGNIZED

378
00:14:05,219 --> 00:14:05,919
FOR THEIR CONTRIBUTIONS.

379
00:14:05,919 --> 00:14:07,755
SO I JUST WANT TO GO THROUGH A

380
00:14:07,755 --> 00:14:09,723
LITTLE BIT QUICKLY, BRAIN BY THE

381
00:14:09,723 --> 00:14:10,190
NUMBERS.

382
00:14:10,190 --> 00:14:12,259
BRAIN CONTINUES TO BE QUITE

383
00:14:12,259 --> 00:14:13,494
PRODUCTIVE.

384
00:14:13,494 --> 00:14:15,796
WE'VE FUNDED ALMOST 1800 PIs

385
00:14:15,796 --> 00:14:18,165
THROUGH ALMOST 1600 AWARDS.

386
00:14:18,165 --> 00:14:19,400
WE'RE STARTING TO FILL IN FROM

387
00:14:19,400 --> 00:14:20,868
THE COAST, THERE'S STILL A BIAS

388
00:14:20,868 --> 00:14:26,707
TOWARD THE EAST AND WEST COAST

389
00:14:26,707 --> 00:14:29,009
BUT WE'RE GETTING MORE IN THE

390
00:14:29,009 --> 00:14:30,210
SOUTHWEST, STILL CONTAINING

391
00:14:30,210 --> 00:14:32,913
ROBUSTS PUBLICATIONS THAT ARE

392
00:14:32,913 --> 00:14:34,381
WIDE DIVERSITY OF JOURNALS AND

393
00:14:34,381 --> 00:14:36,517
WE'RE HAPPY FOR THAT AND WE LOOK

394
00:14:36,517 --> 00:14:37,885
FORWARD TO CONTINUING PROGRESS.

395
00:14:37,885 --> 00:14:41,622
NOW I GUESS THE TOP OF

396
00:14:41,622 --> 00:14:43,223
EVERYBODY'S MIND IS BUJ ELT NOT

397
00:14:43,223 --> 00:14:44,758
JUST FOR BRAIN BUT NIH

398
00:14:44,758 --> 00:14:45,059
GENERALLY.

399
00:14:45,059 --> 00:14:46,760
I CAN JUST SPEAK TO WHAT WE KNOW

400
00:14:46,760 --> 00:14:48,262
ABOUT THE BRAIN BUDGET RIGHT

401
00:14:48,262 --> 00:14:49,296
NOW.

402
00:14:49,296 --> 00:14:50,564
BRAIN FUNDING COMES FROM 2

403
00:14:50,564 --> 00:14:54,468
SOURCES, BASE FUNDING TO THE 10

404
00:14:54,468 --> 00:14:55,169
PARTICIPATING INSTITUTES OR

405
00:14:55,169 --> 00:14:57,071
CENTERS ICDs, SHOWN IN BLUE,

406
00:14:57,071 --> 00:14:59,273
THAT IS WRITTEN STEADILY THROUGH

407
00:14:59,273 --> 00:15:00,774
THE YEARS UP THROUGH 2022 AND

408
00:15:00,774 --> 00:15:02,309
THROUGH THE 21st CENTURY CURES

409
00:15:02,309 --> 00:15:04,044
ACT WE STARTED RECEIVING MONEY

410
00:15:04,044 --> 00:15:06,447
IN 2017 OF VARYING AMOUNTS TO

411
00:15:06,447 --> 00:15:08,882
SUPPLEMENT THE BASE FUNDING.

412
00:15:08,882 --> 00:15:10,918
THAT MONEY EVENTUALLY WILL GO

413
00:15:10,918 --> 00:15:13,220
AWAY IN 2026 BUT YOU WILL NOTICE

414
00:15:13,220 --> 00:15:17,624
THIS JUMP IN THE RED BAR FROM

415
00:15:17,624 --> 00:15:21,829
2022 TO 2023, WHERE THE CURES

416
00:15:21,829 --> 00:15:23,230
AUTHORIZATION JUMPED FROM

417
00:15:23,230 --> 00:15:24,031
142 MILLION TO 450 MILLION AND

418
00:15:24,031 --> 00:15:25,966
TO KEEP OUR BUDGET WITHIN A

419
00:15:25,966 --> 00:15:26,934
REASONABLE RANGE A DECISION WAS

420
00:15:26,934 --> 00:15:31,872
MADE TO TRIM OUR BASE FROM

421
00:15:31,872 --> 00:15:33,841
468 MILLION IN 2022, TO

422
00:15:33,841 --> 00:15:36,543
230 MILLION IN 2023, SO WE

423
00:15:36,543 --> 00:15:40,180
ACTUALLY GOT AN OVERALL

424
00:15:40,180 --> 00:15:41,749
60 MILLION-DOLLAR INCREASE,

425
00:15:41,749 --> 00:15:43,283
ALMOST 10% INCREASE FROM

426
00:15:43,283 --> 00:15:46,220
2022-2023 BUT AS WE PROGRESSED

427
00:15:46,220 --> 00:15:48,922
INTO 2024 THE BASE STAYED LEVEL

428
00:15:48,922 --> 00:15:52,459
AND THE CURES ACT FUNDS, THE

429
00:15:52,459 --> 00:15:54,561
AUTHORIZATION WAS PRESCRIBED TO

430
00:15:54,561 --> 00:15:55,629
DROP DOWN TO 172 MILLION SO

431
00:15:55,629 --> 00:15:58,198
THAT'S HOW WE LANDED AT

432
00:15:58,198 --> 00:15:59,666
402 MILLION IF 2024, AND THEN

433
00:15:59,666 --> 00:16:01,301
THE CONTINUING RESOLUTION, THE

434
00:16:01,301 --> 00:16:02,936
BASE FUNDING STAYS FLAT WHICH IS

435
00:16:02,936 --> 00:16:06,206
230, BUT AGAIN THE CURES FUNDS

436
00:16:06,206 --> 00:16:07,608
DROPPED ANOTHER 81 MILLION TO 91

437
00:16:07,608 --> 00:16:10,511
SO NOW WE'RE AT $321 MILLION FOR

438
00:16:10,511 --> 00:16:10,878
FY25.

439
00:16:10,878 --> 00:16:12,079
SO WE'RE MAKING THE BEST OF IT.

440
00:16:12,079 --> 00:16:13,380
WE STILL HAVE A LOT OF GREAT

441
00:16:13,380 --> 00:16:15,015
SCIENCE WE ARE FUNDING, JUST A

442
00:16:15,015 --> 00:16:16,817
LITTLE BIT LESS THAN WE MIGHT

443
00:16:16,817 --> 00:16:18,685
HAVE WITH MORE FUNDS, BUT AGAIN,

444
00:16:18,685 --> 00:16:21,054
WE ARE COMMITTED TO FUNDING THE

445
00:16:21,054 --> 00:16:24,358
BEST SCIENCE OUT THERE AND WHAT

446
00:16:24,358 --> 00:16:26,160
FY26 HAS TO HOLD FOR US, WE JUST

447
00:16:26,160 --> 00:16:26,860
DON'T KNOW.

448
00:16:26,860 --> 00:16:28,228
YOU CAN STAY UP TO DATE WITH

449
00:16:28,228 --> 00:16:30,731
WHAT WE DO KNOW BY GOING TO OUR

450
00:16:30,731 --> 00:16:33,200
WEBSITE AT THESE PARTICULAR

451
00:16:33,200 --> 00:16:35,335
PAGES.

452
00:16:35,335 --> 00:16:39,540
YOU CAN SCAN THESE QR CODES OR

453
00:16:39,540 --> 00:16:42,042
MORE DIRECTLY GO TO THE WEBSITE,

454
00:16:42,042 --> 00:16:44,278
IT'S PRETTY CLEAR, THERE'S A TAB

455
00:16:44,278 --> 00:16:45,345
THAT SAYS FUNDING AND THAT'S

456
00:16:45,345 --> 00:16:46,713
WHERE THIS INFORMATION IS.

457
00:16:46,713 --> 00:16:48,682
OKAY, SO SINCE WE LAST MET LAST

458
00:16:48,682 --> 00:16:50,651
SUMMER OR FALL, WE'VE BEEN UP TO

459
00:16:50,651 --> 00:16:53,554
A FEW THINGS, SFN HAPPENED,

460
00:16:53,554 --> 00:16:55,289
SOCIETY FOR NEUROSCIENCE ANNUAL

461
00:16:55,289 --> 00:16:57,257
ME MEETING HAPPENED AND WE WERE

462
00:16:57,257 --> 00:16:59,760
DELIGHTED TO BE PART OF THE TOOL

463
00:16:59,760 --> 00:17:01,628
MAKERS SATELLITE EVENT THAT'S

464
00:17:01,628 --> 00:17:11,038
PROMOTED BY THE BRAIN INITIATIVE

465
00:17:11,038 --> 00:17:11,338
 INITTATIVE

466
00:17:11,338 --> 00:17:13,240
FOR THE ALLIANCE, THIS IS WHERE

467
00:17:13,240 --> 00:17:15,309
WE INVITE INVESTIGATORS TO SHOW

468
00:17:15,309 --> 00:17:16,210
THEIR WEARS.

469
00:17:16,210 --> 00:17:18,212
WE HAD 22 TOOLS SHOWN BY 52 TOOL

470
00:17:18,212 --> 00:17:21,081
MAKERS, I THINK THERE WERE 365

471
00:17:21,081 --> 00:17:22,583
ATTENDEES, REALLY WELL ATTENDED

472
00:17:22,583 --> 00:17:23,150
EVENT.

473
00:17:23,150 --> 00:17:24,351
HOPEFULLY PEOPLE WILL HAVE A

474
00:17:24,351 --> 00:17:26,220
USE, THIS IS A WAY TO PICK UP

475
00:17:26,220 --> 00:17:29,389
NEW TOOLS, GET NEW IDEAS AND

476
00:17:29,389 --> 00:17:30,891
EVEN FORGE NEW COLLABORATIONS

477
00:17:30,891 --> 00:17:32,826
AND WE'RE VERY, VERY EXCITED

478
00:17:32,826 --> 00:17:34,461
ABOUT THAT.

479
00:17:34,461 --> 00:17:37,798
ALSO LAST NOVEMBER, WE SPONSORED

480
00:17:37,798 --> 00:17:40,133
A NEUROAI WORKSHOP FROM

481
00:17:40,133 --> 00:17:40,901
NOVEMBER 12th AND 13th.

482
00:17:40,901 --> 00:17:41,768
CAN YOU FIND ALL THIS ON THE WEB

483
00:17:41,768 --> 00:17:44,071
AND THIS IS TO LOOK AT

484
00:17:44,071 --> 00:17:44,905
CONVERGENCE BETWEEN NEUROSCIENCE

485
00:17:44,905 --> 00:17:47,541
AND AI AND HOW WE CAN DRIVE

486
00:17:47,541 --> 00:17:48,976
RECIPROCAL ADVANCES IN

487
00:17:48,976 --> 00:17:49,943
UNDERSTANDING NATURAL

488
00:17:49,943 --> 00:17:51,311
INTELLIGENCE AS WELL AS MACHINE

489
00:17:51,311 --> 00:17:56,149
INTEL VENS.

490
00:17:56,149 --> 00:17:57,484
INTELLIGENCE.

491
00:17:57,484 --> 00:17:59,353
THE WORKSHOP EXPLORED DIVERSE

492
00:17:59,353 --> 00:18:01,455
ELEMENTS AND CONTRASTS WITH AI'S

493
00:18:01,455 --> 00:18:03,357
RELIANCE ON MORE STATIC MODELS.

494
00:18:03,357 --> 00:18:07,227
WE TOUCHED ON THE POTENTIAL OF

495
00:18:07,227 --> 00:18:08,662
NEUROMORPHIC COMPUTING BASICALLY

496
00:18:08,662 --> 00:18:09,630
DESIGNING PLATFORMS BASED ON

497
00:18:09,630 --> 00:18:12,532
WHAT WE UNDERSTAND ABOUT HOW

498
00:18:12,532 --> 00:18:13,600
BIOLOGICAL CIRCUITS WORK AND HOW

499
00:18:13,600 --> 00:18:16,570
WE MIGHT APPLY THOSE IN

500
00:18:16,570 --> 00:18:19,840
BIOMEDICINE AND VERY EXCITING 3,

501
00:18:19,840 --> 00:18:22,442
WE'RE LOOKING AT THE POSSIBILITY

502
00:18:22,442 --> 00:18:23,176
OF LEVERAGING LARGE SCALE BRAIN

503
00:18:23,176 --> 00:18:25,913
DATA WHICH WE ARE DEVELOPING AS

504
00:18:25,913 --> 00:18:28,081
WE SPEAK, OPEN SCIENCE ACROSS

505
00:18:28,081 --> 00:18:29,583
AGENCY COLLABORATIONS AND

506
00:18:29,583 --> 00:18:30,284
COLLABORATIONS THAT INTEREST YOU

507
00:18:30,284 --> 00:18:33,487
AS WELL, TO INSURE THAT WE HAVE

508
00:18:33,487 --> 00:18:35,088
-- WE'RE CONSIDERING IN A

509
00:18:35,088 --> 00:18:36,423
PROACTIVE WAY THE ETHICAL

510
00:18:36,423 --> 00:18:38,158
FRAMEWORKS FOR GUIDING FUTURE

511
00:18:38,158 --> 00:18:38,492
DEVELOPMENTS.

512
00:18:38,492 --> 00:18:41,328
SO WE -- THE WORKSHOP ATTENDEES

513
00:18:41,328 --> 00:18:43,730
HELP TO IDENTIFY PRIORITIES THAT

514
00:18:43,730 --> 00:18:44,564
BALANCE INNOVATION WITH RIGOR.

515
00:18:44,564 --> 00:18:46,199
WHAT ARE THE BIG WEB CONNECTEDS

516
00:18:46,199 --> 00:18:47,701
THAT WE WE MIGHT ADDRESS AND HOW

517
00:18:47,701 --> 00:18:49,670
TO ENABLE SCALE TO ACHIEVE

518
00:18:49,670 --> 00:18:51,571
HEALTH BENEFITS IN ADVANCED

519
00:18:51,571 --> 00:18:53,440
NEUROTECHNOLOGIES AND I THINK

520
00:18:53,440 --> 00:18:56,176
THERE MAY BE GREAT BENEFITS THAT

521
00:18:56,176 --> 00:18:58,245
GO EVEN BEYOND THE HEALTHCARE

522
00:18:58,245 --> 00:18:59,413
REALM AT THE NEUROSCIENCE

523
00:18:59,413 --> 00:18:59,980
REMETABOLISM.

524
00:18:59,980 --> 00:19:01,415
SO THEY'RE VERY EXCITING, IT'S

525
00:19:01,415 --> 00:19:02,282
SOMETHING WE'RE CLEARLY THINKING

526
00:19:02,282 --> 00:19:04,351
ABOUT, IT'S A VERY, VERY HOT

527
00:19:04,351 --> 00:19:06,420
FIELD BUT EVOLVING, FOR I WOULD

528
00:19:06,420 --> 00:19:07,688
SAY, EVOLVING VERY, VERY

529
00:19:07,688 --> 00:19:08,755
RAPIDLY, YOU COULD LEARN MORE

530
00:19:08,755 --> 00:19:10,958
ABOUT WHAT WE FOUND AT THE

531
00:19:10,958 --> 00:19:12,726
WORKSHOP AT THESE SITES.

532
00:19:12,726 --> 00:19:13,927
THERE'S VIDEOCAST, IT'S A LOT TO

533
00:19:13,927 --> 00:19:18,365
WATCH, BUT WE DO HAVE SOME

534
00:19:18,365 --> 00:19:20,968
SUMMARIES YOU MIGHT FIND TO BE

535
00:19:20,968 --> 00:19:24,905
QUITE USEFUL AND FINALLY, AROUND

536
00:19:24,905 --> 00:19:27,674
THE TIME OF SFN, I PUBLISHED A

537
00:19:27,674 --> 00:19:29,977
SHORT PIECE ON BRAINS FIRST 10

538
00:19:29,977 --> 00:19:33,080
YEARS, WHAT THE INITIATIVE HAS

539
00:19:33,080 --> 00:19:34,047
ACCOMPLISHED WITH INPUT AND

540
00:19:34,047 --> 00:19:35,515
DISCOVERIES AND A LOT OF HARD

541
00:19:35,515 --> 00:19:38,618
WORK FROM YOU ALL HERE, AND WITH

542
00:19:38,618 --> 00:19:40,153
AN EYE TOWARD WHAT MIGHT BE COME

543
00:19:40,153 --> 00:19:41,588
NOTHING THE FUTURE BASED ON THE

544
00:19:41,588 --> 00:19:42,289
FIRST 10 YEARS.

545
00:19:42,289 --> 00:19:43,924
SO IT'S A PRETTY QUICK READ, I

546
00:19:43,924 --> 00:19:47,327
INVITE TO YOU COME TAKE A LOOK

547
00:19:47,327 --> 00:19:47,694
AT IT.

548
00:19:47,694 --> 00:19:49,663
OKAY, SO NOW I HAVE A FEW

549
00:19:49,663 --> 00:19:50,297
PROGRAM UPDATES.

550
00:19:50,297 --> 00:19:53,700
THERE'S BEEN A BIT OF CHURN IN

551
00:19:53,700 --> 00:19:55,669
NOTICES OF FUNDING OPPORTUNITIES

552
00:19:55,669 --> 00:19:57,170
OR NOFOs, YOU CAN STAY UP TO

553
00:19:57,170 --> 00:19:58,872
DATE BY GOING TO OUR WEBSITE

554
00:19:58,872 --> 00:19:59,072
HERE.

555
00:19:59,072 --> 00:20:00,307
WE KEEP THIS UP TO DATE AND YOU

556
00:20:00,307 --> 00:20:02,943
CAN USE THE QR CODE OR GO TO

557
00:20:02,943 --> 00:20:04,444
THAT SITE THAT'S LISTED BELOW.

558
00:20:04,444 --> 00:20:06,813
I WANT TO DRAW YOUR ATTENTION TO

559
00:20:06,813 --> 00:20:08,348
1 ACTIVE NOFO, IT IS ACTIVE.

560
00:20:08,348 --> 00:20:10,150
I JUST CHECKED ABOUT AN HOUR

561
00:20:10,150 --> 00:20:11,785
AGO, THIS IS TO PROMOTE HEALTH

562
00:20:11,785 --> 00:20:13,553
THROUGH ALL THROUGH BRAIN

563
00:20:13,553 --> 00:20:14,488
TECHNOLOGY PARTNERSHIPS.

564
00:20:14,488 --> 00:20:15,689
SO BRAIN INITIATIVE RIGHT NOW IS

565
00:20:15,689 --> 00:20:16,757
SUPPORTING THE DEVELOPMENT OF

566
00:20:16,757 --> 00:20:20,160
NEW TOOLS AND RESOURCES, WE HAVE

567
00:20:20,160 --> 00:20:22,229
THE SO CALLED G24 GRANDS THAT

568
00:20:22,229 --> 00:20:23,063
BEFORE DISSEMINATION TO GET

569
00:20:23,063 --> 00:20:25,165
THESE TOOLS OUT THERE, AND 1

570
00:20:25,165 --> 00:20:27,968
CHALLENGE IS TO GET THESE TOOLS

571
00:20:27,968 --> 00:20:29,403
INTO ABOUT THE HANDS OF

572
00:20:29,403 --> 00:20:33,640
RESEARCHERS AT WHAT WE CALL

573
00:20:33,640 --> 00:20:34,875
RESOURCE LIMITED INSTITUTIONS.

574
00:20:34,875 --> 00:20:36,243
SO MANY, MANY PEOPLE ALREADY USE

575
00:20:36,243 --> 00:20:37,844
THESE TOOLS BUT WE'RE CONCERNED

576
00:20:37,844 --> 00:20:39,846
ABOUT IS REALLY TRULY

577
00:20:39,846 --> 00:20:41,448
DEMOCRATIZING THE TOOLS AND

578
00:20:41,448 --> 00:20:42,416
TECHNOLOGIES THAT ARE DEVELOPED

579
00:20:42,416 --> 00:20:45,218
BY BRAIN FOR THE BENEFIT OF ALL.

580
00:20:45,218 --> 00:20:47,721
SO THE IDEA HERE IS FOLKS CAN

581
00:20:47,721 --> 00:20:50,490
COME IN, WITH A GRANT

582
00:20:50,490 --> 00:20:51,558
APPLICATION, LOOKING FOR FUNDING

583
00:20:51,558 --> 00:20:52,826
TO TRANSFER THIS KNOWLEDGE OR

584
00:20:52,826 --> 00:20:56,530
THESE TOOLS INTO A LAB AT A

585
00:20:56,530 --> 00:20:58,932
RESOURCE LIMITED INSTITUTION AS

586
00:20:58,932 --> 00:21:02,269
DEFINED AS THE INNS TUITION

587
00:21:02,269 --> 00:21:05,105
RECEIVING LESS THAN 7 AND HALF

588
00:21:05,105 --> 00:21:06,039
MILLION DOLLARS IN TOTAL COST

589
00:21:06,039 --> 00:21:08,041
FOR THE LAST SEVERAL YEARS.

590
00:21:08,041 --> 00:21:09,409
COUPLE REQUIREMENTS 1 IS THAT

591
00:21:09,409 --> 00:21:11,211
THE CONTACT PI MUST BE AT THE

592
00:21:11,211 --> 00:21:14,281
RESOURCE LIMITED INFORMATION NOT

593
00:21:14,281 --> 00:21:16,783
AT THE RO-1, THAT WELL RESOURCE

594
00:21:16,783 --> 00:21:19,753
UNIVERSITY AND THEN THE KEY

595
00:21:19,753 --> 00:21:20,821
PERSONNEL NEEDS TO INCLUDE THE

596
00:21:20,821 --> 00:21:23,223
FOLKS THAT ARE ACTUALLY

597
00:21:23,223 --> 00:21:24,224
PROVIDING THE VALIDATED

598
00:21:24,224 --> 00:21:25,492
TECHNOLOGY, SO, IT'S A NEW

599
00:21:25,492 --> 00:21:25,725
PROGRAM.

600
00:21:25,725 --> 00:21:28,361
WE HAVE A DUE DATE COMES UP ON

601
00:21:28,361 --> 00:21:30,330
JUNE 17th OF THIS YEAR AND

602
00:21:30,330 --> 00:21:32,299
JUNE 17th OF NEXT YEAR, THE

603
00:21:32,299 --> 00:21:33,233
OTHER CRITERIA LISTED BELOW AND

604
00:21:33,233 --> 00:21:35,235
IF YOU HAVE ANY QUESTIONS, YOU

605
00:21:35,235 --> 00:21:41,708
CAN SCAN THE QR CODE, GO TO THE

606
00:21:41,708 --> 00:21:47,114
PAGE, DR. NATALIE TRZCINSEE IS

607
00:21:47,114 --> 00:21:49,216
HANDLING THIS AND YOU CAN SEE

608
00:21:49,216 --> 00:21:51,485
HERE INNO AT THE BOTTOM.

609
00:21:51,485 --> 00:21:52,986
HOPEFULLY WE CAN GET THIS INTO

610
00:21:52,986 --> 00:21:54,488
MORE HANDS TO PUSH THE FIELD

611
00:21:54,488 --> 00:21:55,088
FORWARD.

612
00:21:55,088 --> 00:21:57,691
SO JUST A BRIEF SUMMARY OF COOL

613
00:21:57,691 --> 00:21:59,126
THINGS GOING ON SINCE WE LAST

614
00:21:59,126 --> 00:21:59,359
SPOKE.

615
00:21:59,359 --> 00:22:01,795
I ALLUDED TO THIS VERY QUICKLY

616
00:22:01,795 --> 00:22:02,028
BEFORE.

617
00:22:02,028 --> 00:22:05,665
THIS IS JUST A VERY BRIEF

618
00:22:05,665 --> 00:22:07,901
SYNOPSIS OF A SUBLICATION FROM

619
00:22:07,901 --> 00:22:09,703
FRANCIS CHAN 1 OF OUR GRANTEES

620
00:22:09,703 --> 00:22:11,438
IN THE BRAIN NEUROETHICS

621
00:22:11,438 --> 00:22:12,005
PORTFOLIO.

622
00:22:12,005 --> 00:22:13,540
NOW WE FOCUS A LOT ON DEVELOPING

623
00:22:13,540 --> 00:22:16,343
THE LATEST AND GREATEST, COOLEST

624
00:22:16,343 --> 00:22:19,312
MOST POWERFUL TECHNOLOGIES IN

625
00:22:19,312 --> 00:22:21,348
VARIOUS DOMAINS INCLUDING

626
00:22:21,348 --> 00:22:22,949
NONINVASIVE HUMAN IMAGING AND

627
00:22:22,949 --> 00:22:24,017
THAT'S GREAT BUT THEN AGAIN THE

628
00:22:24,017 --> 00:22:27,554
ISSUE IS HOW DO WE GET THIS SIS

629
00:22:27,554 --> 00:22:29,756
SEMESTERINATED OUT TO FOLKS THAT

630
00:22:29,756 --> 00:22:31,391
NEED IT.

631
00:22:31,391 --> 00:22:33,326
PURHAPPENS RURAL SETTINGS SO

632
00:22:33,326 --> 00:22:34,161
THEY'RE UNDERSERVED OR DISTANT

633
00:22:34,161 --> 00:22:36,062
FROM THE MAJOR MEDICAL CENTERS,

634
00:22:36,062 --> 00:22:38,331
SO WHAT'S COME UP, WHAT'S COME

635
00:22:38,331 --> 00:22:42,903
UP AND I THINK TODD CONSTABLE

636
00:22:42,903 --> 00:22:44,571
CAN SPEAK TO THIS HAS WELL IS

637
00:22:44,571 --> 00:22:47,507
THE CONCEPT OF DEVELOPING

638
00:22:47,507 --> 00:22:48,575
PORTABLE MRI MACHINES TO EXPAND

639
00:22:48,575 --> 00:22:49,643
THIS POSSIBILITY TO THE BENEFIT

640
00:22:49,643 --> 00:22:51,211
OF PATIENTS WHERE THEY WOULDN'T

641
00:22:51,211 --> 00:22:54,781
OTHERWISE HAVE ACCESS TO IT BUT

642
00:22:54,781 --> 00:22:56,416
IT ALSO INTRODUCES NOVEL

643
00:22:56,416 --> 00:22:58,652
FEATURES INCLUDING NEW USERS,

644
00:22:58,652 --> 00:23:00,987
LOCATIONS, PARTICIPANT POP

645
00:23:00,987 --> 00:23:01,655
OWLINGSS, INCREASED IMAGE

646
00:23:01,655 --> 00:23:04,024
QUALITY, WE DON'T WANT TO JUST

647
00:23:04,024 --> 00:23:05,225
SHIP OUT LESSER TECHNOLOGY, WE

648
00:23:05,225 --> 00:23:06,860
WANT FOLKS TO HAVE ACCESS TO THE

649
00:23:06,860 --> 00:23:09,496
BEST AND HOW DO WE -- HOW DO WE

650
00:23:09,496 --> 00:23:13,633
DEAL WITH THAT AS AN EMERGING

651
00:23:13,633 --> 00:23:14,267
ISSUE.

652
00:23:14,267 --> 00:23:18,972
SO DR. SHEN AND COLLEAGUES AT

653
00:23:18,972 --> 00:23:21,474
THE UNIVERSITY OF MINNESOTA,

654
00:23:21,474 --> 00:23:22,209
THEY IDENTIFIED 15 CORE

655
00:23:22,209 --> 00:23:24,311
CHALLENGES TO BE CONSIDERED AND

656
00:23:24,311 --> 00:23:25,712
DEVELOPED OPERATIONAL GUIDANCE

657
00:23:25,712 --> 00:23:28,448
AND PRACTICAL TOOLS INCLUDING A

658
00:23:28,448 --> 00:23:29,482
NEUROETHICS CHECK LIST, SO IT'S

659
00:23:29,482 --> 00:23:30,450
SOMETHING TO THINK ABOUT AS THEY

660
00:23:30,450 --> 00:23:32,319
MOVE FORWARD AND PROVIDES A

661
00:23:32,319 --> 00:23:33,220
CRITICAL STARTING POINT, IT'S

662
00:23:33,220 --> 00:23:34,821
NOT THE END OF THE STORY BUT

663
00:23:34,821 --> 00:23:35,989
IT'S THE STARTING POINT FOR

664
00:23:35,989 --> 00:23:38,725
RESEARCHERS WHO WISH TO CONDUCT

665
00:23:38,725 --> 00:23:39,626
RESEARCH USING PORTAL

666
00:23:39,626 --> 00:23:40,927
TECHNOLOGIES OUTSIDE OF THE

667
00:23:40,927 --> 00:23:41,928
TRADITIONAL LARGE MEDICAL CENTER

668
00:23:41,928 --> 00:23:43,330
OR LARGE UNIVERSITY SETTINGS

669
00:23:43,330 --> 00:23:45,398
THAT PROVIDE A STARTING POINT

670
00:23:45,398 --> 00:23:46,733
FOR AN INSTITUTIONAL REVIEW

671
00:23:46,733 --> 00:23:47,601
BOARDS, THE COMMUNITY LEADERS,

672
00:23:47,601 --> 00:23:50,437
AS WELL AS FOR THE PRIVATE

673
00:23:50,437 --> 00:23:51,171
SECTOR, NEUROTECHNOLOGY

674
00:23:51,171 --> 00:23:51,871
COMPANIES THAT HOPEFULLY WILL

675
00:23:51,871 --> 00:23:54,741
CONTINUE TO DEVELOP THE NEXT

676
00:23:54,741 --> 00:23:56,109
GENERATION PORTABLE MR TOOLS FOR

677
00:23:56,109 --> 00:23:58,078
USE IN THESE DIFFERENT SETTINGS

678
00:23:58,078 --> 00:23:59,412
SO I ENCOURAGE YOU TO TAKE A

679
00:23:59,412 --> 00:23:59,879
LOOK AT THAT.

680
00:23:59,879 --> 00:24:01,181
I DON'T HAVE A QR CODE BUT YOU

681
00:24:01,181 --> 00:24:04,251
CAN LOOK IT UP IN THIS JOURNAL.

682
00:24:04,251 --> 00:24:04,618
OKAY.

683
00:24:04,618 --> 00:24:06,686
A LOT OF WORK GOING ON IN THE

684
00:24:06,686 --> 00:24:07,988
CELL CENSUS PROGRAM, WE'RE

685
00:24:07,988 --> 00:24:10,156
REALLY THRILL THAD LAST YEAR WAS

686
00:24:10,156 --> 00:24:13,593
LAUNCHED FROM THE BRAIN

687
00:24:13,593 --> 00:24:14,494
INITIATIVE CELL ATLAS NETWORK,

688
00:24:14,494 --> 00:24:15,662
THIS BRAIN KNOWLEDGE PLATFORM

689
00:24:15,662 --> 00:24:17,230
WHICH HAS JUST A TON, A TON OF

690
00:24:17,230 --> 00:24:22,068
DATA AND A GROWING SET OF DATA

691
00:24:22,068 --> 00:24:32,412
FROM THE BI CAN RELEASES AND

692
00:24:32,412 --> 00:24:34,047
OPACCESS DATA AND IT'S GREAT

693
00:24:34,047 --> 00:24:36,016
ACCESS AND TOOLS TO LOOK AT AND

694
00:24:36,016 --> 00:24:37,284
USE IT TO DRIVE THEIR OWN

695
00:24:37,284 --> 00:24:40,086
RESEARCH FORWARD AND AGAIN THIS

696
00:24:40,086 --> 00:24:42,055
FITS WITHIN BRAIN'S ETHOS OF

697
00:24:42,055 --> 00:24:43,556
SUPPORTING OPEN SCIENCE FOR THE

698
00:24:43,556 --> 00:24:47,794
BENEFIT OF AS MANY PEOPLE AS WE

699
00:24:47,794 --> 00:24:48,094
CAN.

700
00:24:48,094 --> 00:24:51,531
ALSO COMING OUT BASED ON THE

701
00:24:51,531 --> 00:24:53,266
FOUNDATION THE BRAIN CELL CENSUS

702
00:24:53,266 --> 00:24:57,370
PROGRAM IS THE -- IS WORKING THE

703
00:24:57,370 --> 00:24:58,805
ALZHEIMER'S RESEARCH SPACE,

704
00:24:58,805 --> 00:25:01,608
USING HIGH QUALITY BRAIN DONOR

705
00:25:01,608 --> 00:25:04,210
-- DONORS FOR A LARGE COHORT

706
00:25:04,210 --> 00:25:05,979
SPANNING FROM LATE STAGES TO

707
00:25:05,979 --> 00:25:07,614
ALZHEIMER'S DISEASE, THIS COMES

708
00:25:07,614 --> 00:25:09,649
FROM THE SEATTLE ALZHEIMER'S

709
00:25:09,649 --> 00:25:11,284
DISEASE BRAIN CELL ATLAS GROUP.

710
00:25:11,284 --> 00:25:19,693
IT'S BEING LED BY DR. ED LEAN

711
00:25:19,693 --> 00:25:26,499
AND @AL AN INSTITUTE AND IT'S

712
00:25:26,499 --> 00:25:28,368
BEEN ENABLED BY MANY TOOLS THAT

713
00:25:28,368 --> 00:25:32,339
CAME OUT OF THE BRAIN

714
00:25:32,339 --> 00:25:34,274
INITIATIVES NETWORK AND DR. LANE

715
00:25:34,274 --> 00:25:36,509
HAS BEEN WORKING ON THESE TOOLS,

716
00:25:36,509 --> 00:25:39,079
IT HAS A DEEP CLINICAL

717
00:25:39,079 --> 00:25:41,715
PHENOTYPES AND THEY FOUND SOME

718
00:25:41,715 --> 00:25:43,350
UNEXPECTED THINGS THAT MIGHT BE

719
00:25:43,350 --> 00:25:45,518
POTENTIALLY RELEVANT AND USEFUL

720
00:25:45,518 --> 00:25:47,954
IN TERMS OF THERAPEUTIC TARGETS

721
00:25:47,954 --> 00:25:50,223
AND FROM THESE ANALYSIS, WHAT

722
00:25:50,223 --> 00:25:52,492
THEY FOUND IS THEY COULD DIVIDE

723
00:25:52,492 --> 00:25:54,427
THE PROGRESSION OF DISEASE INTO

724
00:25:54,427 --> 00:25:59,733
AN EARLY OR QUIET PHASE AND A

725
00:25:59,733 --> 00:26:00,867
LATER RAPIDLY PROGRESSING

726
00:26:00,867 --> 00:26:02,936
SYMPTOMATIC PHASE AND DURING

727
00:26:02,936 --> 00:26:06,005
THIS EARLY QUIET PHASE THEY

728
00:26:06,005 --> 00:26:08,808
FOUND THE SELECTIVE DEATH OF A

729
00:26:08,808 --> 00:26:11,244
SELECTIVE CLASS OF SOPHISTICATED

730
00:26:11,244 --> 00:26:11,945
MATOSTATIN INHIBITORY NEURONS

731
00:26:11,945 --> 00:26:14,247
AND AS WELL AS CHANGES IN OTHER

732
00:26:14,247 --> 00:26:15,715
NONNEURONAL CELLS AND THIS MIGHT

733
00:26:15,715 --> 00:26:17,517
BE AN IDEA EARLY IN THE PROCESS

734
00:26:17,517 --> 00:26:20,587
AND PERHAPS GIVE US A WAY TO

735
00:26:20,587 --> 00:26:23,556
HAVE INTERVENING, SLOWING OR

736
00:26:23,556 --> 00:26:24,557
EVEN PREVENTING DISEASE

737
00:26:24,557 --> 00:26:24,858
PROGRESSION.

738
00:26:24,858 --> 00:26:27,026
AND AGAIN, WITHIN THE ETHOS OF

739
00:26:27,026 --> 00:26:28,428
THE BRAIN INITIATIVE, THEY

740
00:26:28,428 --> 00:26:30,597
PROVIDE OPEN ACCESS TOOLS AND

741
00:26:30,597 --> 00:26:32,465
RESOURCES FOR THE ENTIRE

742
00:26:32,465 --> 00:26:33,133
SCIENTIFIC COMMUNITY.

743
00:26:33,133 --> 00:26:35,001
NOW I'M SURE MALA WILL TELL US A

744
00:26:35,001 --> 00:26:37,303
LOT ABOUT THIS BUT I CAN'T HELP

745
00:26:37,303 --> 00:26:40,540
BUT SHOW THIS COOL ANIMATION, SO

746
00:26:40,540 --> 00:26:42,976
SORRY, MALA I WILL NOT STEAL

747
00:26:42,976 --> 00:26:43,710
YOUR THUNDER, I PROMISE.

748
00:26:43,710 --> 00:26:47,313
WE HAD A NUMBER OF PUBLICATIONS

749
00:26:47,313 --> 00:26:49,149
FROM THE FLY WARE, SUPPORTED BY

750
00:26:49,149 --> 00:26:52,352
BRAIN AND THIS RESULTED IN A

751
00:26:52,352 --> 00:26:54,687
NUMBER OF PAPERS IN NATURE, AND

752
00:26:54,687 --> 00:26:59,592
THESE GORGEOUS REPRESENTATIONS

753
00:26:59,592 --> 00:27:01,194
THAT REPRESENT THE FIRST FULL

754
00:27:01,194 --> 00:27:02,829
RESOLUTION CONNECT OHM OF AN

755
00:27:02,829 --> 00:27:06,399
ADULT ANIMAL IN THIS CASE A

756
00:27:06,399 --> 00:27:07,167
FEMALE DROSOPHILA.

757
00:27:07,167 --> 00:27:10,370
THE GROUP MANAGED TO

758
00:27:10,370 --> 00:27:12,505
CHARACTERIZE 139,000 NEURONS,

759
00:27:12,505 --> 00:27:14,874
FRIEF HUNDRED UNIQUE CELL TYPES,

760
00:27:14,874 --> 00:27:18,144
OVER 50 MILLION SYNAPSES,

761
00:27:18,144 --> 00:27:25,885
RESULTING IN  NONJOINTLICATIONS

762
00:27:25,885 --> 00:27:27,320
IN NATURE, AND IN ADDITION TO

763
00:27:27,320 --> 00:27:31,658
ROUGH ATOM  -- PROVIDING THE

764
00:27:31,658 --> 00:27:33,059
GROUND TRUTH INFORMATION AND THE

765
00:27:33,059 --> 00:27:35,128
ABILITY TO CONSTRAIN HYPOTHESIS

766
00:27:35,128 --> 00:27:36,196
ABOUT NEURAL CIRCUIT FUNCTION AS

767
00:27:36,196 --> 00:27:37,664
THEY RELATE TO DRIVING DEHAD A

768
00:27:37,664 --> 00:27:39,899
FEWS AND I'M SURE MALA WILL TALK

769
00:27:39,899 --> 00:27:41,768
ABOUT THIS AT LENGTH, IT ALSO

770
00:27:41,768 --> 00:27:42,602
INFORMS THE DEVELOPMENT WITH

771
00:27:42,602 --> 00:27:45,872
THESE NEW TOOLS OF LARGE AND

772
00:27:45,872 --> 00:27:46,806
MAMMALIAN WHOLE BRAIN CONNECT

773
00:27:46,806 --> 00:27:48,308
ORDER OF MICRONSS, AND QUICKLY

774
00:27:48,308 --> 00:27:54,481
THROUGH THE  THINGS WE FIND TO

775
00:27:54,481 --> 00:27:58,384
EFFORT, IT'S THE POWER OF

776
00:27:58,384 --> 00:27:59,986
CITIZEN SCIENCE TO HELP PROOF

777
00:27:59,986 --> 00:28:00,887
READ THIS AMAZING COLLECTION OF

778
00:28:00,887 --> 00:28:03,022
DATA AND THE POWER OF OPEN

779
00:28:03,022 --> 00:28:03,556
SCIENCE.

780
00:28:03,556 --> 00:28:05,525
THE FLY WATER GROUP MADE THE

781
00:28:05,525 --> 00:28:07,827
DATA AVAILABLE YEARS BEFORE THIS

782
00:28:07,827 --> 00:28:09,329
PUBLICATION AND WE THINK THAT

783
00:28:09,329 --> 00:28:11,331
ENABLED REALLY COOL PAPERS TO

784
00:28:11,331 --> 00:28:13,266
UBIQUITINNATION PUBLISHED BASED

785
00:28:13,266 --> 00:28:16,102
ON THAT DATA, VERY RICH DATA

786
00:28:16,102 --> 00:28:18,037
RESOURCE WELL BEFORE THESE FOLKS

787
00:28:18,037 --> 00:28:19,105
EVEN PUBLISH IT THEMSELVES.

788
00:28:19,105 --> 00:28:20,840
THIS IS REALLY A TESTAMENT TO

789
00:28:20,840 --> 00:28:22,141
WHAT CAN BE DONE BY SCIENCE AND

790
00:28:22,141 --> 00:28:25,512
SCALE WITH THE IDEA OF LIFTING

791
00:28:25,512 --> 00:28:27,380
ALL THOSE BY GENERATING THESE

792
00:28:27,380 --> 00:28:27,914
OPEN RESOURCES.

793
00:28:27,914 --> 00:28:30,316
AND THEN JUST A FEW WEEKS AGO,

794
00:28:30,316 --> 00:28:32,218
THE MICRONS CONSORTIUM, THE

795
00:28:32,218 --> 00:28:34,187
MACHINE INTELLIGENCE AND

796
00:28:34,187 --> 00:28:38,825
CORTICALE NETWORKS PROJECT JUST

797
00:28:38,825 --> 00:28:44,264
PUBLISHED AN AMAZING CONNECT OME

798
00:28:44,264 --> 00:28:45,632
MAP OF THE VISUAL CORTEX.

799
00:28:45,632 --> 00:28:47,400
I WILL SHOW THE AN MAZE HERE,

800
00:28:47,400 --> 00:28:48,468
THIS IS SO COOL.

801
00:28:48,468 --> 00:28:49,836
THIS A LARGER HUNK OF TISSUE

802
00:28:49,836 --> 00:28:54,941
THAN A FLY BRAIN BUT REALLY

803
00:28:54,941 --> 00:28:55,308
QUITE BEAUTIFUL.

804
00:28:55,308 --> 00:28:56,976
AGAIN, RESULTING IN A PACKAGE OF

805
00:28:56,976 --> 00:29:00,079
PUBLICATIONS IN NATURE AND

806
00:29:00,079 --> 00:29:00,680
NATURE SISTER JOURNALS.

807
00:29:00,680 --> 00:29:02,115
THIS IS ALSO A LARGE TEAM

808
00:29:02,115 --> 00:29:02,482
EFFORT.

809
00:29:02,482 --> 00:29:06,052
THIS WAS A CONSORTIUM OVER 150

810
00:29:06,052 --> 00:29:07,787
RESEARCHERS FUNDED BY BOTH ARPA,

811
00:29:07,787 --> 00:29:09,088
THIS IS WHERE THE PROJECT

812
00:29:09,088 --> 00:29:11,858
STARTED AS A MICRONS PROJECT

813
00:29:11,858 --> 00:29:12,792
FOLLOWING FUNDING FROM NIH

814
00:29:12,792 --> 00:29:14,060
BRAIN.  THIS IS A PROJECT THAT

815
00:29:14,060 --> 00:29:17,830
WAS LED BY INVESTIGATORS FROM 3

816
00:29:17,830 --> 00:29:19,465
BRAIN INSTITUTIONS, THE ALAN

817
00:29:19,465 --> 00:29:20,366
INSTITUTE, PRINCETON UNIVERSITY

818
00:29:20,366 --> 00:29:21,467
AND BAYLOR COLLEGE OF MEDICINE

819
00:29:21,467 --> 00:29:23,303
AND TOGETHER THEY CHARACTERIZE

820
00:29:23,303 --> 00:29:26,306
OVER 2 MILLION CELLS IN THE

821
00:29:26,306 --> 00:29:28,508
CUBIC MILLIMETER ABOUT THE SIZE

822
00:29:28,508 --> 00:29:34,781
OF A BRAIN OF RICE, 4 MILLION

823
00:29:34,781 --> 00:29:35,748
AXOONS OVER 500 MILLION

824
00:29:35,748 --> 00:29:37,150
SYNAPSES, REALLY COOL BUT BEFORE

825
00:29:37,150 --> 00:29:38,451
THEY TOOK THIS 1 TISSUE FOR

826
00:29:38,451 --> 00:29:39,385
PROCESSING, THEY PUT THE MOUSE

827
00:29:39,385 --> 00:29:43,356
IN FRONT OF A VIDEO SCREEN AND

828
00:29:43,356 --> 00:29:47,126
THEY SHOWED IT VARIOUS CLIPS

829
00:29:47,126 --> 00:29:50,063
ANIMATED CLIPS INCLUDING

830
00:29:50,063 --> 00:29:51,698
NATURALISTIC SCENES AND THEY

831
00:29:51,698 --> 00:29:54,200
USING GCAP IMAGING AND THEN THEY

832
00:29:54,200 --> 00:29:56,569
FIXED AND SECTIONED THROUGH THE

833
00:29:56,569 --> 00:29:58,338
PIECE OF CORTEX AND THEY

834
00:29:58,338 --> 00:29:59,238
RECONSTRUCTED IT AND WHAT THEY

835
00:29:59,238 --> 00:30:01,240
WERE ABLE TO DO IS THEY WERE

836
00:30:01,240 --> 00:30:03,610
ABLE TO OVERLAYA ACTIVITY FROM

837
00:30:03,610 --> 00:30:06,713
ABOUT 76,000 NEURONS ON TO THAT

838
00:30:06,713 --> 00:30:07,280
MAP.

839
00:30:07,280 --> 00:30:08,715
SO THEY PROVIDED NOT JUST A

840
00:30:08,715 --> 00:30:12,485
STATIC MAP BUT ALSO A WAY TO

841
00:30:12,485 --> 00:30:14,988
CONNECT FUNCTIONAL RESPONSES

842
00:30:14,988 --> 00:30:16,389
WITH THE ANATOMICAL MAP AND THIS

843
00:30:16,389 --> 00:30:18,124
IS REALLY THE HOLY GRAIL HERE IN

844
00:30:18,124 --> 00:30:21,094
THE FIELD WHICH IS TO BE ABLE TO

845
00:30:21,094 --> 00:30:24,931
LOOK AT ACTIVITY ACROSS THESE

846
00:30:24,931 --> 00:30:26,232
VASTLY COMPLEX NETWORKS SO

847
00:30:26,232 --> 00:30:27,834
REALLY GREAT START TO THIS

848
00:30:27,834 --> 00:30:28,201
FIELD.

849
00:30:28,201 --> 00:30:30,136
WE'RE VERY, VERY EXCITED ABOUT

850
00:30:30,136 --> 00:30:30,570
THIS.

851
00:30:30,570 --> 00:30:31,904
THEY'VE AGAIN DEVELOPED AN OPEN

852
00:30:31,904 --> 00:30:34,107
RESOURCE FOR DEVELOPING MODELS

853
00:30:34,107 --> 00:30:34,807
OF NEURAL COMPUTATIONS, AGAIN

854
00:30:34,807 --> 00:30:36,542
THE NAME OF THE GAME HERE IS

855
00:30:36,542 --> 00:30:37,610
OPEN RESOURCES, AND THIS WILL

856
00:30:37,610 --> 00:30:39,379
SET THE STAGE FOR THE BRAIN

857
00:30:39,379 --> 00:30:41,314
CONNECTS PROJECT WHICH WE ARE

858
00:30:41,314 --> 00:30:49,689
WORKING VERY ACTIVELY ON AS WE

859
00:30:49,689 --> 00:30:49,889
SPEAK.

860
00:30:49,889 --> 00:30:52,325
WITH THAT, IT'S GETTING TO THE

861
00:30:52,325 --> 00:30:52,625
END.

862
00:30:52,625 --> 00:30:53,960
YOU CAN FIND REQUESTING ON THE

863
00:30:53,960 --> 00:30:55,895
WEBSITE, WE PUBLISH NOTES ON THE

864
00:30:55,895 --> 00:30:57,430
BRAIN BLOG, SO CHECK-IN THERE

865
00:30:57,430 --> 00:30:59,165
FROM TIME TO TIME AND GUIDE

866
00:30:59,165 --> 00:31:02,235
NOTICES AND OF COURSE YOUR

867
00:31:02,235 --> 00:31:02,969
ABOUTEST RESOURCE FOR THE BRAIN

868
00:31:02,969 --> 00:31:04,537
IN TERMS OF FUNDING OPPORTUNITY

869
00:31:04,537 --> 00:31:05,605
SYSTEM TO FIND YOUR BRAIN

870
00:31:05,605 --> 00:31:06,673
PROGRAM OFFICER AND THAT'S

871
00:31:06,673 --> 00:31:08,074
LISTED AS WELL ON OUR BRAIN

872
00:31:08,074 --> 00:31:10,143
WEBSITE THAT YOU CAN FIND AT

873
00:31:10,143 --> 00:31:10,610
BRAIN .GOV.

874
00:31:10,610 --> 00:31:12,578
SO I WILL STOP THERE, SUSAN DO

875
00:31:12,578 --> 00:31:13,946
WE HAVE TIME FOR A FEW QUESTIONS

876
00:31:13,946 --> 00:31:16,616
BEFORE WE MOVE ON TO MALA'S

877
00:31:16,616 --> 00:31:16,849
TALK.

878
00:31:16,849 --> 00:31:20,386
YES, WE ACTUALLY HAVE QUITE A

879
00:31:20,386 --> 00:31:21,721
BIT OF TIME.

880
00:31:21,721 --> 00:31:23,489
SO, YEAH.

881
00:31:23,489 --> 00:31:25,658
BRIAN?

882
00:31:25,658 --> 00:31:25,958
>> BRYAN?

883
00:31:25,958 --> 00:31:27,560
>> BRYAN, HIBRYAN? 

884
00:31:27,560 --> 00:31:28,394
>> HI, GREETINGS.

885
00:31:28,394 --> 00:31:34,667
SO, 1 THING THAT WOULD BE VERY

886
00:31:34,667 --> 00:31:35,635
USEFUL SOPHISTICATED TO -- MY

887
00:31:35,635 --> 00:31:41,908
WIFE IS COMING IN HERE --

888
00:31:41,908 --> 00:31:42,709
>> HI, BRIAN'S WIFE.

889
00:31:42,709 --> 00:31:44,877
>> YOU CAN SEE SHE BROKE
HER

890
00:31:44,877 --> 00:31:45,044
ARM.

891
00:31:45,044 --> 00:31:46,979
>> BUT YOU STILL HAVE THE
DOG,

892
00:31:46,979 --> 00:31:47,180
GOOD.

893
00:31:47,180 --> 00:31:52,852
>> IT'S A GOOD THING.

894
00:31:52,852 --> 00:31:55,488
>> SO I WAS RECENTLY GAVE
A

895
00:31:55,488 --> 00:32:00,226
PUBLIC TALK IN ALL PLACES,

896
00:32:00,226 --> 00:32:02,695
MONTANA WHERE I WENT FOR

897
00:32:02,695 --> 00:32:05,665
UNDERGRAD AND I HIGHLIGHTED THIS

898
00:32:05,665 --> 00:32:08,601
AMAZING RESOURCE THAT WAS JUST

899
00:32:08,601 --> 00:32:12,205
PUBLISHED IN NATURE.

900
00:32:12,205 --> 00:32:15,208
IT'S TRULY ASTOUNDING BUT WHAT

901
00:32:15,208 --> 00:32:18,845
I'M ASKING IS FOR -- IS IT

902
00:32:18,845 --> 00:32:21,414
POSSIBLE THAT EITHER FOLKS AT

903
00:32:21,414 --> 00:32:25,885
THE NIH OR PEOPLE IN THE MICRONS

904
00:32:25,885 --> 00:32:28,020
COULD MAKE A SUMMARY VIDEO

905
00:32:28,020 --> 00:32:30,022
THAT'S HIGH QUALITY THAT, WE

906
00:32:30,022 --> 00:32:32,625
COULD SHARE, THAT SHOWS, YOU

907
00:32:32,625 --> 00:32:35,561
KNOW WHAT YOU JUST SHOWED,

908
00:32:35,561 --> 00:32:35,928
BASICALLY?

909
00:32:35,928 --> 00:32:38,097
SO I WAS ABLE TO, YOU KNOW I

910
00:32:38,097 --> 00:32:40,166
DOWNLOADED THE VIDEOS THAT THEY

911
00:32:40,166 --> 00:32:43,503
HAVE AND EDITED THEM BUT IT

912
00:32:43,503 --> 00:32:44,604
WASN'T QUITE WHAT I WAS LOOKING

913
00:32:44,604 --> 00:32:46,506
FOR SO IT WILL BE REALLY NICE IF

914
00:32:46,506 --> 00:32:49,308
THEY COULD PROVIDE SOMETHING

915
00:32:49,308 --> 00:32:51,677
LIKE THAT FOR US TO USE WHEN WE

916
00:32:51,677 --> 00:32:53,479
GIVE TALKS BECAUSE IT'S JUST --

917
00:32:53,479 --> 00:32:57,650
I MEAN IT'S AN AMAZING

918
00:32:57,650 --> 00:32:58,684
ACHIEVEMENT, FOLKS.

919
00:32:58,684 --> 00:32:59,652
>> YEAH, YEAH.

920
00:32:59,652 --> 00:33:02,088
>> I WOULD REALLY LIKE TO

921
00:33:02,088 --> 00:33:02,688
PUBLICIZE IT.

922
00:33:02,688 --> 00:33:05,291
>> YEAH, SO I PUT THAT
VIDEO IN,

923
00:33:05,291 --> 00:33:07,026
I WILL CONFESS, IT'S NOT THE

924
00:33:07,026 --> 00:33:08,327
HIGHEST QUALITY VIDEO, I JUST

925
00:33:08,327 --> 00:33:09,529
DIDN'T GET AROUND TO BOTHERING

926
00:33:09,529 --> 00:33:11,597
FOLKS FOR IT BUT YOU CAN CONTACT

927
00:33:11,597 --> 00:33:18,704
THE FEEMSA THE THE ALAN

928
00:33:18,704 --> 00:33:19,872
INSTITUTE, THEY'VE BEEN GREAT

929
00:33:19,872 --> 00:33:20,907
ABOUT SHARING THESE RESOURCES,

930
00:33:20,907 --> 00:33:22,408
THANK YOU FOR GETTING THE WORD

931
00:33:22,408 --> 00:33:23,276
ABOUT ABOUT THE SCIENCE ISSUES

932
00:33:23,276 --> 00:33:24,844
THE MORE PEOPLE THAT KNOW ABOUT

933
00:33:24,844 --> 00:33:25,845
THE RESOURCE, THE MORE PEOPLE

934
00:33:25,845 --> 00:33:28,981
WILL BE USING IT SO I WILL

935
00:33:28,981 --> 00:33:30,883
SUGGEST CONTACTING THEM, SINCE I

936
00:33:30,883 --> 00:33:32,351
DOWNLOADED THAT 1 LITTLE CLIP IN

937
00:33:32,351 --> 00:33:33,753
A VERY AMATEURISH WAY, I'M SURE

938
00:33:33,753 --> 00:33:36,355
YOU CAN FIND A BETTER COPY.

939
00:33:36,355 --> 00:33:38,090
MALA, MAYBE YOU CAN HELP.

940
00:33:38,090 --> 00:33:40,359
>> I WILL JUST CHIME IN
AND SAY

941
00:33:40,359 --> 00:33:43,229
YOU SHOULD E-MAIL AMY STERLING,

942
00:33:43,229 --> 00:33:45,565
SHE MAKES THE VIDEOS FOR THE

943
00:33:45,565 --> 00:33:47,066
FLYWIRE AND THE MICRONS PROJECT,

944
00:33:47,066 --> 00:33:48,467
SHE WILL GIVE YOU WHAT YOU NEED

945
00:33:48,467 --> 00:33:49,635
SO JUST E-MAIL HER.

946
00:33:49,635 --> 00:33:50,803
>> COULD YOU PUT HER
E-MAIL

947
00:33:50,803 --> 00:33:52,238
ADDRESS IN THE CHAT.

948
00:33:52,238 --> 00:33:52,572
>> WILL DO.

949
00:33:52,572 --> 00:33:53,773
>> THAT WOULD BE AWESOME
AND LET

950
00:33:53,773 --> 00:33:56,475
HER KNOW SHE WILL BE GETTING

951
00:33:56,475 --> 00:33:56,809
SOME E-MAILS.

952
00:33:56,809 --> 00:33:58,511
>> YEAH, AND WE CAN FOLLOW
UP

953
00:33:58,511 --> 00:34:01,948
AND DISTRIBUTE IT TO THE REST OF

954
00:34:01,948 --> 00:34:02,782
THE MCWG.

955
00:34:02,782 --> 00:34:06,652
>> I MEAN IT'S ASTOUNDING
THANK

956
00:34:06,652 --> 00:34:06,819
YOU.

957
00:34:06,819 --> 00:34:08,154
>> THANKS, BRIAN YOUR
BEING UP

958
00:34:08,154 --> 00:34:09,956
THERE AS A GREAT AMBASSADOR FOR

959
00:34:09,956 --> 00:34:13,559
THE PROGRAM. 

960
00:34:13,559 --> 00:34:18,197
>> OTHER QUESTIONS OR
COMMENTS?

961
00:34:18,197 --> 00:34:20,967
>> YEAH, I LOVE TO SEE
THOSE

962
00:34:20,967 --> 00:34:22,568
VIDEOS, TOO, BUT I WAS TRYING TO

963
00:34:22,568 --> 00:34:24,103
CHAT THAT BUT THANK YOU.

964
00:34:24,103 --> 00:34:26,839
>> YEAH IF YOU GO TO THE
MICRONS

965
00:34:26,839 --> 00:34:27,473
WEBSITE, IT'S THERE.

966
00:34:27,473 --> 00:34:29,809
IF YOU GO TO THE LANDING PAGE ON

967
00:34:29,809 --> 00:34:31,143
NATURE -- NATURE HAS A LANDING

968
00:34:31,143 --> 00:34:32,144
PAGE, THERE'S A BEAUTIFUL

969
00:34:32,144 --> 00:34:33,446
LANDING PAGE THERE AS WELL.

970
00:34:33,446 --> 00:34:35,181
A LOT OF GREAT STUFF.

971
00:34:35,181 --> 00:34:39,018
BUT YOU KNOW AGAIN, YOU SHOULD

972
00:34:39,018 --> 00:34:41,854
CONTACT -- I DIDN'T WANT TO

973
00:34:41,854 --> 00:34:43,756
SURVEY ME AND MYSELF AND AMY

974
00:34:43,756 --> 00:34:44,590
STERLING SHE'S MADE AMAZING

975
00:34:44,590 --> 00:34:50,997
PIECES THAT CAN BE USED.

976
00:34:50,997 --> 00:34:55,735
WE'RE VERY EXCITED TO SEE THESE

977
00:34:55,735 --> 00:34:56,903
BIG PROJECTS, I DON'T WANT TO

978
00:34:56,903 --> 00:34:59,138
SAY COME TO FRUITION, THEY'VE

979
00:34:59,138 --> 00:35:00,740
REACHED A POINT WHERE THEY CAN

980
00:35:00,740 --> 00:35:03,276
BE USED AND DEVELOP FURTHER AND

981
00:35:03,276 --> 00:35:04,443
AGAIN LONG THE WHOLE CONTINUUM

982
00:35:04,443 --> 00:35:08,814
OF DEVELOPING THESE MAPS INTO

983
00:35:08,814 --> 00:35:10,683
OTHER OR SIMILARLY OR EVEN MORE

984
00:35:10,683 --> 00:35:11,684
COMPLEX SYSTEMS WILL BE FUN TO

985
00:35:11,684 --> 00:35:16,389
WATCH OVER THE NEXT COUPLE OF

986
00:35:16,389 --> 00:35:17,056
YEARS.

987
00:35:17,056 --> 00:35:18,324
OKAY.

988
00:35:18,324 --> 00:35:21,160
ANYBODY ELSE?

989
00:35:21,160 --> 00:35:23,429
>> ANY OTHER QUESTIONS?

990
00:35:23,429 --> 00:35:24,630
>> ANY QUESTIONS?

991
00:35:24,630 --> 00:35:26,198
>> MORE EXCITING STUFF
COMING

992
00:35:26,198 --> 00:35:28,401
FROM OUR OTHER CONSORTIA, SO

993
00:35:28,401 --> 00:35:29,802
KEEP OUR EYES OUT.

994
00:35:29,802 --> 00:35:30,836
>>  I HAVE 1 FOLLOW UP
QUESTION

995
00:35:30,836 --> 00:35:32,939
TO THAT ACTUALLY IF THERE'S A

996
00:35:32,939 --> 00:35:34,807
MOMENT WHICH IS THAT THESE

997
00:35:34,807 --> 00:35:35,775
RESOURCES SEEM REALLY FABULOUS

998
00:35:35,775 --> 00:35:37,243
AND WITH THE FEES AND OTHERS

999
00:35:37,243 --> 00:35:42,415
LIKE THE ALAN BRAIN PROJECT, I'M

1000
00:35:42,415 --> 00:35:46,352
WONDERING IF THERE ARE EXISTING

1001
00:35:46,352 --> 00:35:48,120
THINGS IN PLACE FOR TRAINING

1002
00:35:48,120 --> 00:35:49,956
COURSES AND BEING ABLE TO ACCESS

1003
00:35:49,956 --> 00:35:50,589
THESE RESOURCES BECAUSE THAT

1004
00:35:50,589 --> 00:35:52,358
WOULD BE GREAT FOR THE GROUPS I

1005
00:35:52,358 --> 00:35:52,658
KNOW OF.

1006
00:35:52,658 --> 00:35:53,159
>> RIGHT.

1007
00:35:53,159 --> 00:35:54,527
SO WE ARE SUPPORTING A NUMBER OF

1008
00:35:54,527 --> 00:35:56,796
THESE, YOU WILL HAVE TO -- LET'S

1009
00:35:56,796 --> 00:36:00,199
SEE HOW CAN WE MAKE THAT MORE

1010
00:36:00,199 --> 00:36:00,566
OBVIOUS?

1011
00:36:00,566 --> 00:36:04,503
ARE THEY ON OUR WEBSITE NOW?

1012
00:36:04,503 --> 00:36:06,639
THE FOLKS THAT ARE BEING FUNDED

1013
00:36:06,639 --> 00:36:09,608
FOR THESE COURSES THEY DO

1014
00:36:09,608 --> 00:36:14,447
ACCEPTED  -- SEND OUT NOTICES

1015
00:36:14,447 --> 00:36:18,284
INVITING PEOPLE TO PARTAKE.

1016
00:36:18,284 --> 00:36:19,585
LET'S SEE.

1017
00:36:19,585 --> 00:36:21,487
MAYBE 1 OF MY -- 1 OF OUR GREAT

1018
00:36:21,487 --> 00:36:24,390
STAFF COULD TELL YOU HOW TO LOOK

1019
00:36:24,390 --> 00:36:25,691
FOR THAT.

1020
00:36:25,691 --> 00:36:27,693
CRYSTAL ARE YOU AROUND?

1021
00:36:27,693 --> 00:36:28,260
>> YEAH I'M ACTUALLY JUST

1022
00:36:28,260 --> 00:36:31,297
PULLING IT UP ON 1 OF OUR FUNDED

1023
00:36:31,297 --> 00:36:33,566
R25S WHICH ARE SHORT COURSES

1024
00:36:33,566 --> 00:36:34,600
THAT COVER SOME OF THESE

1025
00:36:34,600 --> 00:36:37,603
ACTIVITIES AND I WILL DROP THE

1026
00:36:37,603 --> 00:36:38,537
LINK IN THE CHAT.

1027
00:36:38,537 --> 00:36:42,875
>> SO WHERE MIGHT 1 FIND
THAT ON

1028
00:36:42,875 --> 00:36:43,609
OUR WEBSITE?

1029
00:36:43,609 --> 00:36:45,511
SINCE THE CHAT DOESN'T GO OUT TO

1030
00:36:45,511 --> 00:36:45,845
THE VIDEOCAST.

1031
00:36:45,845 --> 00:36:48,581
>> YEAH, SO IF YOU GO TO
OUR

1032
00:36:48,581 --> 00:36:49,849
WEBSITE BRAIN .GOV AND GO TO

1033
00:36:49,849 --> 00:36:51,317
FUNDING ON THE DROP DOWN MENU,

1034
00:36:51,317 --> 00:36:53,853
THERE'S A SECTION THERE THAT

1035
00:36:53,853 --> 00:36:55,021
SAYS STUNTED AWARDS, IF YOU

1036
00:36:55,021 --> 00:36:57,289
CLICK ON THAT IT WILL COME WITH

1037
00:36:57,289 --> 00:36:58,758
A LISTING OF ALL OF OUR FUNDED

1038
00:36:58,758 --> 00:37:01,394
AWARDS TO DATE AND YOU SHOULD BE

1039
00:37:01,394 --> 00:37:05,998
ABLE TO SEARCH R25 TO FIND

1040
00:37:05,998 --> 00:37:09,335
R-SHORT COURSES.

1041
00:37:09,335 --> 00:37:09,935
>> THANKS CRYSTAL.

1042
00:37:09,935 --> 00:37:11,771
THANK YOU TOR FOR BRINGING THAT

1043
00:37:11,771 --> 00:37:12,138
UP.

1044
00:37:12,138 --> 00:37:13,472
THESE ARE WAYS WE ARE TRYING TO

1045
00:37:13,472 --> 00:37:18,711
GET THE STUFF OUT THERE.

1046
00:37:18,711 --> 00:37:19,011
YEAH.

1047
00:37:19,011 --> 00:37:21,213
OKAY. 

1048
00:37:21,213 --> 00:37:24,583
ANY OTHER QUESTIONS.

1049
00:37:24,583 --> 00:37:26,185
MALA, ARE YOU READY IN.

1050
00:37:26,185 --> 00:37:28,854
>> YES, I THINK SO.

1051
00:37:28,854 --> 00:37:32,558
WELL I'M GOING TO SAY A FEW NICE

1052
00:37:32,558 --> 00:37:33,893
THINGS FIRST SO HOLD ON.

1053
00:37:33,893 --> 00:37:35,961
>> I WILL WAIT TO SHARE MY

1054
00:37:35,961 --> 00:37:36,228
SCREEN.

1055
00:37:36,228 --> 00:37:37,496
>> YEAH, KUWAIT SO AGAIN
I'M

1056
00:37:37,496 --> 00:37:40,933
DELIGHTED TO HAVE DR. MALA

1057
00:37:40,933 --> 00:37:42,501
MURTHY GIVE OUR SCIENCE TALK, WE

1058
00:37:42,501 --> 00:37:43,769
TRY TO HAVE A SCIENCE TALK FROM

1059
00:37:43,769 --> 00:37:45,871
1 OF OUR MEMBERS FROM TIME TO

1060
00:37:45,871 --> 00:37:46,338
TIME.

1061
00:37:46,338 --> 00:37:47,973
MALA IS A PROFESSOR OF

1062
00:37:47,973 --> 00:37:48,607
NEUROSCIENCE AT PRINCE TOLL-LIKE

1063
00:37:48,607 --> 00:37:50,009
RECEPTOR UNIVERSITY AND ALSO

1064
00:37:50,009 --> 00:37:52,411
DIRECTOR OF THE PRINCETON

1065
00:37:52,411 --> 00:37:53,612
NEUROSCIENCE INSTITUTE SINCE

1066
00:37:53,612 --> 00:37:59,952
JULY OF 22, SHE RECEIVED BATCHER

1067
00:37:59,952 --> 00:38:01,420
CEIVED --

1068
00:38:01,420 --> 00:38:03,456
BACHELOR'S DEGREE FROM M. I.T.

1069
00:38:03,456 --> 00:38:11,330
AND SHE RECEIVED HER Ph.D.

1070
00:38:11,330 --> 00:38:15,101
DOING STUFF IN THE BEGINNING OF

1071
00:38:15,101 --> 00:38:18,370
HER PLAYING OUT INTEREST IN

1072
00:38:18,370 --> 00:38:19,872
SENSORS AND NEUROSCIENCE USING

1073
00:38:19,872 --> 00:38:21,207
DROSOPHILA SOPILLA USE A MODEL

1074
00:38:21,207 --> 00:38:21,807
SYSTEM.

1075
00:38:21,807 --> 00:38:23,375
SHE'S DONE BRILLIANT WORK THERE.

1076
00:38:23,375 --> 00:38:25,644
SHE'S KNOWN FOR THE PIONEERING

1077
00:38:25,644 --> 00:38:27,913
WORK IN UNRALPHING THE CIRCUIT

1078
00:38:27,913 --> 00:38:29,115
AND COMPUTATIONAL BASIS OF

1079
00:38:29,115 --> 00:38:30,382
SOCIAL BEHAVIORS ISSUES SHE'S

1080
00:38:30,382 --> 00:38:33,552
BUILT AN INCREDIBLE GROUP OF

1081
00:38:33,552 --> 00:38:35,521
COMPUTATIONAL SCIENTISTS AND

1082
00:38:35,521 --> 00:38:36,188
EXPERIMENTALISTS USING

1083
00:38:36,188 --> 00:38:37,890
DISCOVERED SOME OF THE SENSORRED

1084
00:38:37,890 --> 00:38:40,526
FEEDBACK USE AND BRAIN INTERNAL

1085
00:38:40,526 --> 00:38:43,295
STATES THAT MODULATE SONG

1086
00:38:43,295 --> 00:38:43,996
PATTERNS IN FLIES.

1087
00:38:43,996 --> 00:38:45,965
LEVERAGING THE AWESOME HAD --

1088
00:38:45,965 --> 00:38:48,300
NOT JUST THE AWESOME POWER OF

1089
00:38:48,300 --> 00:38:50,202
GENETICS AND ALSO APPLYING AND

1090
00:38:50,202 --> 00:38:51,270
INVENTING AND APPLYING REALLY

1091
00:38:51,270 --> 00:38:53,772
COOL METHODS FOR QUANTIFYING

1092
00:38:53,772 --> 00:38:54,406
ANIMAL BEHAVIOR.

1093
00:38:54,406 --> 00:38:56,008
AND MANY OF THESE METHODS HAD

1094
00:38:56,008 --> 00:38:58,978
BEEN APPLIED MORE BROADLY ACROSS

1095
00:38:58,978 --> 00:39:01,814
THE FIELD, NOT JUST IN THIS 1

1096
00:39:01,814 --> 00:39:03,415
SYSTEM, AND AGAIN REALLY

1097
00:39:03,415 --> 00:39:05,518
INTEGRATING ALL OF HER WORK

1098
00:39:05,518 --> 00:39:06,285
THEORY.

1099
00:39:06,285 --> 00:39:10,456
AS MENTIONED MALA ALSO CO-LED

1100
00:39:10,456 --> 00:39:14,093
THE FLY WIRE CONSOWRTIUM WHICH I

1101
00:39:14,093 --> 00:39:16,395
JUST TOUCHED ON A FEW MINUTES

1102
00:39:16,395 --> 00:39:16,662
AGO.

1103
00:39:16,662 --> 00:39:19,999
MALA HAS RECEIVED A NUMBER OF

1104
00:39:19,999 --> 00:39:22,168
AWARDS, NIH INNOVATOR AWARD,

1105
00:39:22,168 --> 00:39:24,236
CLEAN FOUNDATION, Mc KNIGHT

1106
00:39:24,236 --> 00:39:25,704
SCHOLAR AWARD, NINDS RESEARCH

1107
00:39:25,704 --> 00:39:29,909
PROGRAM AWARD, A BUNCH OF

1108
00:39:29,909 --> 00:39:32,211
AWARDS, JUST THE EARN FOR THE

1109
00:39:32,211 --> 00:39:33,479
NIH BRAIN INITIATIVE AND YOU'VE

1110
00:39:33,479 --> 00:39:36,582
ALREADY SEEN WORK THAT'S FUNDED

1111
00:39:36,582 --> 00:39:38,617
THROUGH BRAIN TO MALA AND HER

1112
00:39:38,617 --> 00:39:39,518
COLLEAGUES BUT YOU MIGHT HEAR

1113
00:39:39,518 --> 00:39:43,522
MORE ABOUT THAT IN A MINUTE AND

1114
00:39:43,522 --> 00:39:47,126
ALSO AN HHMI SCHOLAR ARK WARD,

1115
00:39:47,126 --> 00:39:48,861
AND SCIENTIST AWARD AMONG

1116
00:39:48,861 --> 00:39:49,395
OTHERS.

1117
00:39:49,395 --> 00:39:50,095
SHE PARTICIPATED FOR SEVERAL

1118
00:39:50,095 --> 00:39:54,166
EVENTS FOR THE BRAIN INITIATIVE,

1119
00:39:54,166 --> 00:39:56,035
M ALA, I CAN'T THANK YOU FOR FOR

1120
00:39:56,035 --> 00:39:57,903
THAT, AND HERE SHE IS WITH THE

1121
00:39:57,903 --> 00:39:59,972
BRAIN WORKING GROUP SINCE 2021.

1122
00:39:59,972 --> 00:40:01,373
SO LOOKING FORWARD AS ALWAYS TO

1123
00:40:01,373 --> 00:40:04,210
HEARING AND SEEING 1 OFIOURE

1124
00:40:04,210 --> 00:40:04,410
TALKS.

1125
00:40:04,410 --> 00:40:06,679
TALKS -- 1 OF YOUR TALKS.

1126
00:40:06,679 --> 00:40:09,281
IT'S ALL YOURS.

1127
00:40:09,281 --> 00:40:09,848
>> THANK YOU JOHN.

1128
00:40:09,848 --> 00:40:11,550
AND THANK YOU FOR THE EXTENSIVE

1129
00:40:11,550 --> 00:40:14,353
INTRODUCTION, I DON'T KNOW THAT

1130
00:40:14,353 --> 00:40:15,821
IT WAS FULLY DESERVED BUT I

1131
00:40:15,821 --> 00:40:16,155
APPRECIATE IT.

1132
00:40:16,155 --> 00:40:20,226
LET ME SEE IF I CAN SHARE MY

1133
00:40:20,226 --> 00:40:24,463
SCREEN IN MS TEAMS, GIVE ME A

1134
00:40:24,463 --> 00:40:24,863
SECOND.

1135
00:40:24,863 --> 00:40:26,465
DO YOU SEE MY SCREEN?

1136
00:40:26,465 --> 00:40:28,834
>> LOOKS GOOD.

1137
00:40:28,834 --> 00:40:29,435
>> OKAY, IT'S FUZZY.

1138
00:40:29,435 --> 00:40:31,904
>> IS IT FUZZY TO
EVERYBODY

1139
00:40:31,904 --> 00:40:32,137
ELSE.

1140
00:40:32,137 --> 00:40:33,839
>> IT'S FUZZY, AND YOU'RE

1141
00:40:33,839 --> 00:40:36,642
BREAKING UP A LITTLE, YOU'RE

1142
00:40:36,642 --> 00:40:38,143
KIND OF PIXELATED AS WELL.

1143
00:40:38,143 --> 00:40:39,445
>> I'M IN MY OFFICE WHICH
WILL

1144
00:40:39,445 --> 00:40:42,648
SURPRISE ME IF I DON'T HAVE GOOD

1145
00:40:42,648 --> 00:40:42,881
WHYIFY.

1146
00:40:42,881 --> 00:40:44,183
THAT'S BETTER.

1147
00:40:44,183 --> 00:40:45,317
>> THAT'S PRETTY FUZZY.

1148
00:40:45,317 --> 00:40:45,718
>> BUT NOT YOU.

1149
00:40:45,718 --> 00:40:50,089
THE SLIDE IS BETTER.

1150
00:40:50,089 --> 00:40:55,294
>> I TOOK THE SLIDE DOWN. 

1151
00:40:55,294 --> 00:41:02,001
NDESKTOP'S REALLY FUZZY. 

1152
00:41:02,001 --> 00:41:12,311
>> IT'S NOT YOU.

1153
00:41:22,821 --> 00:41:25,524
DOES THAT LOOK BETTER?

1154
00:41:25,524 --> 00:41:25,791
>> NO.

1155
00:41:25,791 --> 00:41:29,928
>> IT WAS WORKING EARLIER?

1156
00:41:29,928 --> 00:41:31,263
>> IT WAS.

1157
00:41:31,263 --> 00:41:32,898
>> WHY DON'T YOU STOP
SHARING

1158
00:41:32,898 --> 00:41:34,600
YOUR VIDEO, LIKE YOUR FACE

1159
00:41:34,600 --> 00:41:39,738
CAMERA, AND THEN PRESENT AND SEE

1160
00:41:39,738 --> 00:41:49,982
IF THAT WORKS.

1161
00:41:58,590 --> 00:41:58,891
>> OKAY.

1162
00:41:58,891 --> 00:42:00,626
>> DO WE HAVE A COPY OF
YOUR

1163
00:42:00,626 --> 00:42:05,731
SLIDES, WOULD IT BE EASIER IF WE

1164
00:42:05,731 --> 00:42:06,699
PRESENT THEM FOR YOU.

1165
00:42:06,699 --> 00:42:08,200
>> LET ME TRY 1 MORE
THING.

1166
00:42:08,200 --> 00:42:13,472
>> LET ME SEE IF I SHARE
MY

1167
00:42:13,472 --> 00:42:13,806
WINDOW.

1168
00:42:13,806 --> 00:42:16,542
OKAY, DO YOU SEE THAT, DOES THAT

1169
00:42:16,542 --> 00:42:19,078
LOOK ANY BETTER?

1170
00:42:19,078 --> 00:42:21,814
>> UNFORTUNATELY, NO.

1171
00:42:21,814 --> 00:42:23,515
>> OKAY, MALA, YOU MAY
ALSO WANT

1172
00:42:23,515 --> 00:42:25,384
TO TRY LOGGING OFF AND THEN

1173
00:42:25,384 --> 00:42:26,085
COMING BACK IN.

1174
00:42:26,085 --> 00:42:27,753
SOMETIMES THAT CAN HELP SOME OF

1175
00:42:27,753 --> 00:42:28,487
THE CONNECTIVITY ISSUES AND WE

1176
00:42:28,487 --> 00:42:30,522
WILL KEEP AN EYE OUT FOR YOU.

1177
00:42:30,522 --> 00:42:32,558
>> OKAY, I WILL DO THAT,
HOLD

1178
00:42:32,558 --> 00:42:32,858
ON.

1179
00:42:32,858 --> 00:42:43,235
>> SEE YOU IN A BIT.

1180
00:42:54,046 --> 00:42:55,280
>> WE DIDN'T HAVE ANY
ISSUES IN

1181
00:42:55,280 --> 00:42:58,817
THE TEST RUN JUST A HALF HOUR

1182
00:42:58,817 --> 00:43:01,587
AGO, DID WE? 

1183
00:43:01,587 --> 00:43:04,256
>> NO.

1184
00:43:04,256 --> 00:43:08,394
NO.

1185
00:43:08,394 --> 00:43:11,230
>> I BLAME TEAMS. 

1186
00:43:11,230 --> 00:43:11,864
>> I KNOW.

1187
00:43:11,864 --> 00:43:13,665
I THINK WE'RE ALL A LITTLE

1188
00:43:13,665 --> 00:43:14,166
FRUSTRATED WITH TEAMS.

1189
00:43:14,166 --> 00:43:15,801
I GUESS WE WILL GET USED TO IT

1190
00:43:15,801 --> 00:43:18,270
SINCE WE DON'T HAVE A CHOICE.

1191
00:43:18,270 --> 00:43:20,339
>> WE GOT USED TO ZOOM, WE
WILL

1192
00:43:20,339 --> 00:43:21,874
GET USED TO THIS.

1193
00:43:21,874 --> 00:43:24,643
>> I AM BACK. 

1194
00:43:24,643 --> 00:43:25,811
>> OKAY, YOU'RE BACK.

1195
00:43:25,811 --> 00:43:34,086
>> NOW WE WILL TRY 1 MORE
TIME. 

1196
00:43:34,086 --> 00:43:35,821
>> YOU'RE CLEARER.

1197
00:43:35,821 --> 00:43:36,221
YEAH, MUCH BETTER.

1198
00:43:36,221 --> 00:43:36,555
>> NO.

1199
00:43:36,555 --> 00:43:42,227
>> IT WAS BETTER A MINUTE
AGO.

1200
00:43:42,227 --> 00:43:43,262
>> IT'S STILL THE SAME.

1201
00:43:43,262 --> 00:43:53,839
>> TRY TURNING YOUR CAMERA
OFF.

1202
00:43:59,111 --> 00:43:59,478
>> OKAY.

1203
00:43:59,478 --> 00:44:00,946
I DON'T HAVE A SOLUTION.

1204
00:44:00,946 --> 00:44:06,418
>> BUT IT REALLY LOOKS
BAD, YOU

1205
00:44:06,418 --> 00:44:07,753
CAN'T SEE ANYTHING.

1206
00:44:07,753 --> 00:44:09,621
>> CAN YOU SEND THE SLIDES
OVER?

1207
00:44:09,621 --> 00:44:12,591
>> WOULD YOU LET US TRY TO
SHARE

1208
00:44:12,591 --> 00:44:12,958
IT MAYBE?

1209
00:44:12,958 --> 00:44:13,625
>> SURE.

1210
00:44:13,625 --> 00:44:14,159
OKAY, HOLD IT.

1211
00:44:14,159 --> 00:44:15,861
>> JOHN YOU MIGHT NEED TO
DO IT

1212
00:44:15,861 --> 00:44:17,863
BECAUSE I THINK THE SLIDES ARE

1213
00:44:17,863 --> 00:44:18,897
IN C-NOTE.

1214
00:44:18,897 --> 00:44:23,335
>> I DON'T HAVE KEY NOTE. 

1215
00:44:23,335 --> 00:44:26,238
>> I DON'T EITHER.

1216
00:44:26,238 --> 00:44:28,540
LEE CHEN HAS KEY NOTE.

1217
00:44:28,540 --> 00:44:30,876
>> IT WAS WORKING EARLIER.

1218
00:44:30,876 --> 00:44:34,379
>> I HAVE KEY NOTE, I CAN
TRY IF

1219
00:44:34,379 --> 00:44:36,715
YOU SEND IT TO ME MALA.

1220
00:44:36,715 --> 00:44:38,717
HOW SHOULD I SEND IT TO YOU.

1221
00:44:38,717 --> 00:44:39,418
IT'S QUITE LARGE.

1222
00:44:39,418 --> 00:44:41,520
>> YEAH, I HAVE A DROP BOX
LINK,

1223
00:44:41,520 --> 00:44:43,155
I WILL SHARE THAT WITH YOU.

1224
00:44:43,155 --> 00:44:48,827
>> OKAY, I AM LOOKING
FORWARD TO

1225
00:44:48,827 --> 00:44:49,962
YOUR TALK.

1226
00:44:49,962 --> 00:44:52,431
>> MAYBE CAN GIVE MALA'S
TALK,

1227
00:44:52,431 --> 00:44:53,999
JUST KIDDING N. JUST KIDDING!

1228
00:44:53,999 --> 00:44:54,199
THAT?

1229
00:44:54,199 --> 00:44:56,168
THAT WOULD BE TOTALLY FINE WITH

1230
00:44:56,168 --> 00:44:59,204
ME, IF HE GAVE ME TALK.

1231
00:44:59,204 --> 00:44:59,505
>> OKAY.

1232
00:44:59,505 --> 00:45:02,841
>> IME SURE HE WILL GIVE
AN EVEN

1233
00:45:02,841 --> 00:45:03,475
BETTER TALK.

1234
00:45:03,475 --> 00:45:11,316
OKAY, I DON'T HAVE YOU IN MY

1235
00:45:11,316 --> 00:45:12,417
AUTOFILL.

1236
00:45:12,417 --> 00:45:14,953
>> LUO@STANFORD.EDU.

1237
00:45:14,953 --> 00:45:15,220
>> OKAY.

1238
00:45:15,220 --> 00:45:17,189
THIS IS TEAM SCIENCE IN ACTION,

1239
00:45:17,189 --> 00:45:27,332
FOLKS. 

1240
00:45:28,433 --> 00:45:28,934
>> THIS IS CRAZY.

1241
00:45:28,934 --> 00:45:34,273
>> ALL OF A SUDDEN THE
SLIDE

1242
00:45:34,273 --> 00:45:34,606
LOOKS BETTER.

1243
00:45:34,606 --> 00:45:36,208
WHY DON'T YOU SEND IT AND WE CAN

1244
00:45:36,208 --> 00:45:37,175
TRY IN THE MEAN TIME.

1245
00:45:37,175 --> 00:45:39,177
>> IT WILL TAKE MAYBE A
MINUTE

1246
00:45:39,177 --> 00:45:40,345
OR 2.

1247
00:45:40,345 --> 00:45:41,847
>> DOES THIS LOOK BETTER.

1248
00:45:41,847 --> 00:45:52,457
>> IT LOOKS GOOD, IT LOOKS
GOOD 

1249
00:45:57,996 --> 00:45:59,865
TONIGHT TOUCH ANYTHING WHY DON'T

1250
00:45:59,865 --> 00:46:02,267
YOU SEND IT TO LI CHEN AND THEN

1251
00:46:02,267 --> 00:46:04,736
WHEN WE GETS GOING WE CAN SWITCH

1252
00:46:04,736 --> 00:46:04,937
OVER.

1253
00:46:04,937 --> 00:46:06,538
>> I DON'T KNOW WHY I'M
HAVING

1254
00:46:06,538 --> 00:46:07,306
AN ISSUE.

1255
00:46:07,306 --> 00:46:10,542
>> SOMETHING MUST BE
UNSTABLE.

1256
00:46:10,542 --> 00:46:12,811
>> SO I HAVE ANOTHER
COMPUTER I

1257
00:46:12,811 --> 00:46:15,314
COULD ALSO TRY JOINING FROM THIS

1258
00:46:15,314 --> 00:46:15,581
COMPUTER.

1259
00:46:15,581 --> 00:46:21,687
DO YOU WANT TO ME TRY THAT?

1260
00:46:21,687 --> 00:46:27,392
YOU GUYS TALK AMONGST YOURSELF.

1261
00:46:27,392 --> 00:46:33,231
OKAY, WHILE I E-MAIL I CHEN. 

1262
00:46:33,231 --> 00:46:41,306
-- LI CHEN.

1263
00:46:41,306 --> 00:46:43,342
I SENT IT TO YOU.

1264
00:46:43,342 --> 00:46:53,885
I WILL TRY TO JOIN MS TEAMS FROM

1265
00:47:08,500 --> 00:47:08,700
HERE.

1266
00:47:08,700 --> 00:47:10,235
>> YOUR TALK IS HERE, I
GOT IT,

1267
00:47:10,235 --> 00:47:12,938
I NEED TO DOWNLOAD IT.

1268
00:47:12,938 --> 00:47:14,640
>> I'M JUST LOOKING FOR
THE LINK

1269
00:47:14,640 --> 00:47:16,174
TO THE MEETING TODAY, SO I CAN

1270
00:47:16,174 --> 00:47:26,718
JOIN ON MY OTHER COMPUTER, WHERE

1271
00:47:45,103 --> 00:47:46,338
IS THAT?

1272
00:47:46,338 --> 00:47:47,272
>> MALA, IF IT HELPS, IT'S
IN

1273
00:47:47,272 --> 00:47:50,709
THE CHAT AS WELL, THE MEETING

1274
00:47:50,709 --> 00:47:53,111
INFORMATION FOR YOU TO COPY.

1275
00:47:53,111 --> 00:47:55,013
WE JUST POSTED IT.

1276
00:47:55,013 --> 00:47:56,481
>> YEAH, I TRIED TO I
DIDN'T

1277
00:47:56,481 --> 00:47:58,316
EVEN FROM MY OTHER COMPUTER,

1278
00:47:58,316 --> 00:47:59,885
IT'S NOT -- IT'S NOT LETTING ME.

1279
00:47:59,885 --> 00:48:10,328
I GET A SIGN THAT SAYS REGISTER.

1280
00:48:10,328 --> 00:48:11,763
YEAH, YOU WILL NEED TO JOIN FROM

1281
00:48:11,763 --> 00:48:13,065
THE E-MAIL LINK THAT YOU WERE

1282
00:48:13,065 --> 00:48:18,537
SENT WHEN YOU REGISTERED, IT'S A

1283
00:48:18,537 --> 00:48:28,780
PERK OF TEAMS.

1284
00:49:25,604 --> 00:49:25,837
LET ME TRY.

1285
00:49:25,837 --> 00:49:27,639
>> I WILL JOIN FROM MY
OTHER

1286
00:49:27,639 --> 00:49:30,408
COMPUTER, IT SAID I HAD EXCEEDED

1287
00:49:30,408 --> 00:49:31,643
THE NUMBER OF DEVICES.

1288
00:49:31,643 --> 00:49:37,215
THAT DID NOT WORK.

1289
00:49:37,215 --> 00:49:37,482
>> OKAY.

1290
00:49:37,482 --> 00:49:39,751
>> IT SHOULD BE AT THE TOP
OF

1291
00:49:39,751 --> 00:49:40,719
YOUR INBOX AS WELL.

1292
00:49:40,719 --> 00:49:51,096
NLET ME TRY TO SHARE. 

1293
00:49:53,665 --> 00:49:54,633
OKAY, TRY IT.

1294
00:49:54,633 --> 00:49:55,901
>> I'M NOT VERY FAMILIAR
WITH

1295
00:49:55,901 --> 00:50:06,344
THE MICROSOFT THING EMPLOY

1296
00:50:39,978 --> 00:50:40,545
>> SO STRANGE.

1297
00:50:40,545 --> 00:50:42,380
>> I'M NOT SURE THIS WILL
HELP

1298
00:50:42,380 --> 00:50:43,682
BUT IN MY EXPERIENCE IT DOES

1299
00:50:43,682 --> 00:50:45,317
MAKERS AND A DIFFERENCE IF YOU

1300
00:50:45,317 --> 00:50:47,185
HAVE TEAMS DOWNLOADED ON YOUR

1301
00:50:47,185 --> 00:50:48,186
COMPUTER OR IF YOU ARE JUST

1302
00:50:48,186 --> 00:50:49,855
USING IT ON THE BROWSER.

1303
00:50:49,855 --> 00:50:51,523
>> WELL I WAS USING ON THE

1304
00:50:51,523 --> 00:50:53,725
BROWSER, I DO HAVE THE

1305
00:50:53,725 --> 00:50:55,293
APPLICATION BUT I BEING NOT

1306
00:50:55,293 --> 00:50:57,195
FIGURE OUT HOW TO OPEN THIS

1307
00:50:57,195 --> 00:50:57,696
MEETING IN THE APP.

1308
00:50:57,696 --> 00:50:59,965
SO I DO HAVE MICROSOFT TEAMS, I

1309
00:50:59,965 --> 00:51:01,433
COULD SWITCH OVER TO THAT BUT I

1310
00:51:01,433 --> 00:51:11,910
DON'T KNOW HOW TO JOIN THIS

1311
00:51:13,111 --> 00:51:15,847
MEETING THERE. 

1312
00:51:15,847 --> 00:51:26,358
>> IT LOOKS LIKE LI CHEN
IS

1313
00:51:33,732 --> 00:51:34,165
BACK.

1314
00:51:34,165 --> 00:51:35,333
>> THANK YOU EVERYONE FOR
YOUR

1315
00:51:35,333 --> 00:51:37,135
PATIENCE, CAN YOU SEE WE'RE

1316
00:51:37,135 --> 00:51:40,038
STILL NAVIGATING THIS.

1317
00:51:40,038 --> 00:51:50,548
EVERY MEETING IS AN ADVENTURE.

1318
00:52:16,708 --> 00:52:17,943
I CAN TRY 1 MORE TIME TO SHARE

1319
00:52:17,943 --> 00:52:24,816
MY SCREEN IN THE HOPES IT MIGHT

1320
00:52:24,816 --> 00:52:25,050
WORK.

1321
00:52:25,050 --> 00:52:26,918
BUT IT'S STILL BLURRY.

1322
00:52:26,918 --> 00:52:28,620
THERE MUST BE SOME REASON IT WAS

1323
00:52:28,620 --> 00:52:32,157
BLURRY NOW BUT IT WASN'T BEFORE.

1324
00:52:32,157 --> 00:52:35,660
>> IT WENT REALLY BLURRY.

1325
00:52:35,660 --> 00:52:37,062
HAVE YOU CLOSED OTHER

1326
00:52:37,062 --> 00:52:37,996
APPLICATIONS BY ANY CHANCE,

1327
00:52:37,996 --> 00:52:48,506
SORRY TO MAKE YOU GUYS HELP ME

1328
00:52:49,507 --> 00:52:49,774
TROUBLE SHOOT.

1329
00:52:49,774 --> 00:52:51,276
LINKING ICF CHEN IS BACK AND HE

1330
00:52:51,276 --> 00:52:52,544
WANTS TO TRY SHARING.

1331
00:52:52,544 --> 00:52:55,313
>> YOU WANT TO UNSHARE
MALA.

1332
00:52:55,313 --> 00:52:56,081
>> YEAH, STOP SHARING.

1333
00:52:56,081 --> 00:52:59,284
BUT IT'S STILL BLURRY IS THAT

1334
00:52:59,284 --> 00:53:01,519
RIGHT? 

1335
00:53:01,519 --> 00:53:04,356
>> YES.

1336
00:53:04,356 --> 00:53:04,923
>> OKAY.

1337
00:53:04,923 --> 00:53:06,591
>> HI, I'M BACK, I WAS
DOWN

1338
00:53:06,591 --> 00:53:10,328
GRADED NOW I'M UPGRADED.

1339
00:53:10,328 --> 00:53:13,965
I'M GOING TO TRY TO SHARE.

1340
00:53:13,965 --> 00:53:15,500
>> YOU HAVE THE UPGRADE. 

1341
00:53:15,500 --> 00:53:20,038
>> OKAY, TRY SHARING.

1342
00:53:20,038 --> 00:53:21,072
>> THAT LOOKS GOOD.

1343
00:53:21,072 --> 00:53:21,506
>> YEAH.

1344
00:53:21,506 --> 00:53:23,241
>> DO YOU SEE MY SCREEN?

1345
00:53:23,241 --> 00:53:26,378
>> WE CAN, IT'S VERY
SHARP.

1346
00:53:26,378 --> 00:53:27,912
>> VERY SHARP.

1347
00:53:27,912 --> 00:53:28,213
OKAY.

1348
00:53:28,213 --> 00:53:30,015
>> IT'S SHARP, CAN YOU GET
IT

1349
00:53:30,015 --> 00:53:40,425
INTO PRESENTATION MODE.

1350
00:53:43,161 --> 00:53:49,234
>> LOOKS GOOD BUT I HAVE
TO

1351
00:53:49,234 --> 00:53:56,041
PROMPT LI CHEN TO SHARE THE

1352
00:53:56,041 --> 00:53:56,341
SLIDES.

1353
00:53:56,341 --> 00:53:57,642
>> ALL RIGHT, I WILL GO
BACK.

1354
00:53:57,642 --> 00:53:59,177
>> WE WILL JUST WORK WITH
WHAT

1355
00:53:59,177 --> 00:54:00,712
WE HAVE HERE.

1356
00:54:00,712 --> 00:54:01,813
>> OKAY, LOOKS GREAT.

1357
00:54:01,813 --> 00:54:02,480
>> YEAH.

1358
00:54:02,480 --> 00:54:02,680
OKAY.

1359
00:54:02,680 --> 00:54:04,082
>> YEAH, SO VERY SORRY, I
DON'T

1360
00:54:04,082 --> 00:54:05,917
KNOW WHAT'S GOING ON, I'M IN MY

1361
00:54:05,917 --> 00:54:08,253
OFFICE SO I THINK THE INTERNET

1362
00:54:08,253 --> 00:54:10,355
AT PRINCETON SHOULD BE GOOD

1363
00:54:10,355 --> 00:54:10,588
ENOUGH.

1364
00:54:10,588 --> 00:54:11,189
>> I'M SORRY.

1365
00:54:11,189 --> 00:54:14,759
THIS IS OUR FIRST BIG THING WITH

1366
00:54:14,759 --> 00:54:16,861
TEAMS AND OBVIOUSLY IT'S

1367
00:54:16,861 --> 00:54:17,095
GLITCHY.

1368
00:54:17,095 --> 00:54:18,863
>> WELL WE WILL JUST GO
AHEAD,

1369
00:54:18,863 --> 00:54:20,398
SO WITH NO FURTHER ADO, THANK

1370
00:54:20,398 --> 00:54:23,134
YOU JOHN FOR ASKING ME TO TALK

1371
00:54:23,134 --> 00:54:25,303
ABOUT MY SCIENCE.

1372
00:54:25,303 --> 00:54:27,338
I'VE BEEN A MEMBER -- I'VE BEEN

1373
00:54:27,338 --> 00:54:28,473
INVOLVED IN THE BRAIN INITIATIVE

1374
00:54:28,473 --> 00:54:30,942
I WOULD SAY FROM THE VERY

1375
00:54:30,942 --> 00:54:32,110
BEGINNING AND IT'S BEEN A

1376
00:54:32,110 --> 00:54:33,878
PLEASURE TO SERVE ON THE MCWG

1377
00:54:33,878 --> 00:54:35,547
FOR TD LAST FEW YEARS.

1378
00:54:35,547 --> 00:54:37,015
AND AS JOHN MENTIONED, A LOT OF

1379
00:54:37,015 --> 00:54:39,184
THE WORK I WILL TELL YOU ABOUT

1380
00:54:39,184 --> 00:54:41,186
HAS BEEN FUNDED BY THE BRAIN

1381
00:54:41,186 --> 00:54:42,854
INITIATIVE SO I'M VERY

1382
00:54:42,854 --> 00:54:43,888
APPRECIATIVE FOR THE SUPPORT

1383
00:54:43,888 --> 00:54:45,490
WE'VE HAD AND I ALSO JUST WANTED

1384
00:54:45,490 --> 00:54:48,993
TO START OFF SAYING THANK YOU TO

1385
00:54:48,993 --> 00:54:51,329
THE NIH STAFF WHO'S HERE TODAY,

1386
00:54:51,329 --> 00:54:53,198
WE ALL REALLY VALUE YOUR HARD

1387
00:54:53,198 --> 00:54:56,034
WORK AND THANKS AGAIN.

1388
00:54:56,034 --> 00:54:57,435
OKAY, LI, CHEN LET'S MOVE

1389
00:54:57,435 --> 00:54:57,669
FORWARD.

1390
00:54:57,669 --> 00:55:06,111
SO AS JOHN MENTIONED THERE WERE

1391
00:55:06,111 --> 00:55:08,279
A SERIES OF PAPERS WE PUBLISHED

1392
00:55:08,279 --> 00:55:12,050
LAST YEAR ON THE WIDE BRAIN

1393
00:55:12,050 --> 00:55:12,817
CONNECT O--METABOLIZED.

1394
00:55:12,817 --> 00:55:17,188
AND THIS WAS THE FIRST WHOLE

1395
00:55:17,188 --> 00:55:18,156
BRAIN CONNECT OME FOR

1396
00:55:18,156 --> 00:55:19,724
DROSOPHILA, SO I THOUGHT I WOULD

1397
00:55:19,724 --> 00:55:21,392
WALK YOU THROUGH WHY WE

1398
00:55:21,392 --> 00:55:24,195
CONNECTED, HOW WE GENERATED AND

1399
00:55:24,195 --> 00:55:25,530
IT HOW IT'S BEING USED AND GOING

1400
00:55:25,530 --> 00:55:28,566
TO BE USED AND I APOLOGIZE IN

1401
00:55:28,566 --> 00:55:30,368
ADVANCE LI CHEN, THERE'S A LOT

1402
00:55:30,368 --> 00:55:33,037
OF ANIMATIONS IN THIS SHOW BUT

1403
00:55:33,037 --> 00:55:36,341
JUST DO YOUR BEST TO FORWARD, SO

1404
00:55:36,341 --> 00:55:39,644
JUST KEEP GOING.

1405
00:55:39,644 --> 00:55:39,844
OKAY.

1406
00:55:39,844 --> 00:55:42,647
SO YEAH WHAT I WANTED TO SAY IS

1407
00:55:42,647 --> 00:55:45,884
THAT SORT OF THE HISTORY OF

1408
00:55:45,884 --> 00:55:46,684
NEUROSCIENCE AND SYSTEMS

1409
00:55:46,684 --> 00:55:48,553
NEUROSCIENCE IN PARTICULAR HAS

1410
00:55:48,553 --> 00:55:50,355
BEEN TO LINK INDIVIDUAL CELL

1411
00:55:50,355 --> 00:55:52,390
TYPES, SMALL CIRCUITS AND BRAIN

1412
00:55:52,390 --> 00:55:53,892
AREAS BEHAVIOR AND WE'VE DONE A

1413
00:55:53,892 --> 00:55:55,827
REALLY EXCELLENT JOB OF THAT AND

1414
00:55:55,827 --> 00:55:57,228
THERE'S BEEN YOU KNOW A LOT OF

1415
00:55:57,228 --> 00:55:59,330
WORK SUPPORTED THROUGH THE BRAIN

1416
00:55:59,330 --> 00:56:01,065
INITIATIVE IN THIS AREA, BUT THE

1417
00:56:01,065 --> 00:56:02,033
CHALLENGE WE'RE FACING AGAIN, OH

1418
00:56:02,033 --> 00:56:03,468
THIS WILL BE A GREAT TASK FOR

1419
00:56:03,468 --> 00:56:04,869
YOU LI CHEN TO FIGURE OUT WHEN I

1420
00:56:04,869 --> 00:56:06,271
WANT TO MOVE FORWARD, THIS IS

1421
00:56:06,271 --> 00:56:07,672
GREAT, YOU WILL HAVE A LOT OF

1422
00:56:07,672 --> 00:56:13,011
FUN, SO YOU'RE DOING A GREAT JOB

1423
00:56:13,011 --> 00:56:13,578
SO FAR.

1424
00:56:13,578 --> 00:56:15,313
SO WE KNOW THAT THESE CELL TYPE

1425
00:56:15,313 --> 00:56:16,915
CIRCUITS AND BRAIN AREAS ARE

1426
00:56:16,915 --> 00:56:18,483
EMBEDDED WITHIN A MUCH MORE

1427
00:56:18,483 --> 00:56:19,918
COMPLEX NETWORK AND WAWE REALLY

1428
00:56:19,918 --> 00:56:22,820
NEED NOW ARE WAYS TO CONNECT

1429
00:56:22,820 --> 00:56:23,555
THESE DISTRIBUTED MECHANISMS TOY

1430
00:56:23,555 --> 00:56:24,189
ABOUT HAD A FEW.

1431
00:56:24,189 --> 00:56:26,424
AND I WOULD SAY THE CRITICAL

1432
00:56:26,424 --> 00:56:28,393
THING IS FIRST FIGURING OUT THE

1433
00:56:28,393 --> 00:56:30,361
COMPLETE MAP AND THAT'S WHERE

1434
00:56:30,361 --> 00:56:31,529
CONNECT ORDER OF MICRONSICS CAN

1435
00:56:31,529 --> 00:56:33,164
HELP US OF WIRING AND THEN

1436
00:56:33,164 --> 00:56:35,133
THERE'S SORT OF THE MAJOR

1437
00:56:35,133 --> 00:56:37,068
CHALLENGE OF ONCE WE HAVE THE

1438
00:56:37,068 --> 00:56:37,969
WIRING, LINKING THEM SO THAT'S

1439
00:56:37,969 --> 00:56:41,539
WHAT I WANT TO SHARE WITH YOU

1440
00:56:41,539 --> 00:56:46,778
TODAY.

1441
00:56:46,778 --> 00:56:54,652
SO, MY LAB HAS BEEN USING 

1442
00:56:54,652 --> 00:56:55,220
DROSOPHILA COMMUNICATIONS

1443
00:56:55,220 --> 00:56:57,055
ASPECTS AND AS JOHN MENTIONED

1444
00:56:57,055 --> 00:56:59,490
WE'VE BEEN TAKING THESE ARROWS

1445
00:56:59,490 --> 00:57:01,192
MECHANISTICALLY TRYING TO

1446
00:57:01,192 --> 00:57:01,859
UNDERSTAND THE COMPLEX FEEDBACK

1447
00:57:01,859 --> 00:57:04,329
BETWEEN MALES AND FEMALES.

1448
00:57:04,329 --> 00:57:07,599
SO THE SONG THE MALES PRODUCE IS

1449
00:57:07,599 --> 00:57:09,801
HIGHLY VARIABLE, HE NEVER SINGS

1450
00:57:09,801 --> 00:57:11,202
THE SAME SONG TWICE AS WE

1451
00:57:11,202 --> 00:57:12,604
DISCOVERED BUT THE RULES OF THE

1452
00:57:12,604 --> 00:57:13,605
BEHAVIOR CAN BE LEARNED BY

1453
00:57:13,605 --> 00:57:14,939
THINKING ABOUT THE INTERACTION

1454
00:57:14,939 --> 00:57:16,708
BETWEEN THESE 2 ANIMALS AND

1455
00:57:16,708 --> 00:57:18,543
USING THE SENSORY FEEDBACK AS A

1456
00:57:18,543 --> 00:57:20,245
PREDICTER ON BEHAVIOR AND WE'VE

1457
00:57:20,245 --> 00:57:22,714
MADE A LOT OF HEAD WAY THAT WAY.

1458
00:57:22,714 --> 00:57:24,716
BUT 1 THING I WILL TELL YOU

1459
00:57:24,716 --> 00:57:26,217
ABOUT SONG IS THAT THERE'S 2

1460
00:57:26,217 --> 00:57:28,620
TYPES OF SONG THE MALES SING SO

1461
00:57:28,620 --> 00:57:30,321
WHEN HE'S FURTHER FROM THE

1462
00:57:30,321 --> 00:57:32,390
FEMALE HE SINGS A SIMPLE PULSE

1463
00:57:32,390 --> 00:57:33,524
ONLY SONG AS CAN YOU SEE HERE

1464
00:57:33,524 --> 00:57:37,095
AND WHEN HE'S NEAR THE FEMALE HE

1465
00:57:37,095 --> 00:57:39,497
SINGS A MORE COMPLEX SONG WHERE

1466
00:57:39,497 --> 00:57:42,834
HE ALTERNATES BETWEEN 2 MAIN

1467
00:57:42,834 --> 00:57:45,470
MODES, PULSE AND SIGNS AND THESE

1468
00:57:45,470 --> 00:57:45,870
LONGER SEQUENCES.

1469
00:57:45,870 --> 00:57:47,138
YOU CAN KEEP GOING EMPLOY SO I

1470
00:57:47,138 --> 00:57:50,241
WANTED TO SHARE WITH YOU AN

1471
00:57:50,241 --> 00:57:51,142
EARLIER RESULT, PRECONNECT OME

1472
00:57:51,142 --> 00:57:54,012
TO MOTIVATE WHERE WE NEEDED THIS

1473
00:57:54,012 --> 00:57:55,146
WHOLE BRAIN CONNECT OME.

1474
00:57:55,146 --> 00:57:57,482
AND IN THIS SET OF EXPERIMENTS

1475
00:57:57,482 --> 00:57:59,183
WE WERE CATALOGING NEURAL

1476
00:57:59,183 --> 00:58:00,752
ACTIVITY THROUGHOUT THE BRAIN.

1477
00:58:00,752 --> 00:58:02,520
SO THE GOAL OF THE EXPERIMENT

1478
00:58:02,520 --> 00:58:05,023
WAS TO IMAGE 2-PHOTON MICROSCOPY

1479
00:58:05,023 --> 00:58:05,690
AT HIGH RESOLUTION, SMALL

1480
00:58:05,690 --> 00:58:08,126
VOLUMES OF THE BRAIN AND

1481
00:58:08,126 --> 00:58:10,128
PRECISELY STITCH THEM TOGETHER

1482
00:58:10,128 --> 00:58:11,562
IN AN ATLAS, AND IN EACH

1483
00:58:11,562 --> 00:58:12,830
EXPERIENCE WE WERE PRESENTING

1484
00:58:12,830 --> 00:58:14,532
THESE 2 TYPES OF SONG, PULSE AND

1485
00:58:14,532 --> 00:58:16,934
SIGN, AND LOOKING AT BRAIN

1486
00:58:16,934 --> 00:58:21,105
ACTIVITY AND BRAIN ACTIVITY WAS

1487
00:58:21,105 --> 00:58:22,974
BEING MEASURED WITH GCAMP

1488
00:58:22,974 --> 00:58:24,409
EXPRESSED EVERYWHERE SO HERE

1489
00:58:24,409 --> 00:58:27,779
IT'S REPORTED AS ROIs OR

1490
00:58:27,779 --> 00:58:28,246
REGIONS OF INTEREST.

1491
00:58:28,246 --> 00:58:29,447
YOU CAN KEEP GOING.

1492
00:58:29,447 --> 00:58:32,517
OKAY, SO WHEN WE MAPPED THIS

1493
00:58:32,517 --> 00:58:34,986
ACTIVITY, YOU KNOW 19,000 ROIs

1494
00:58:34,986 --> 00:58:35,620
ACROSS 33 FLIES.

1495
00:58:35,620 --> 00:58:39,223
WE FOUND IT SPANNED THE ENTIRETY

1496
00:58:39,223 --> 00:58:49,767
OF THE 3 ACCESS THAT DEFINE THE

1497
00:58:51,703 --> 00:58:52,870
BRAIN HERE, IT'S IN EVERY PART

1498
00:58:52,870 --> 00:58:53,404
OF THE BRAIN.

1499
00:58:53,404 --> 00:58:54,238
YOU CAN KEEP GOING.

1500
00:58:54,238 --> 00:58:56,607
AND IF WE CLUSTER THE ACTIVITY

1501
00:58:56,607 --> 00:58:58,976
INTO FUNCTIONAL RESPONSE TYPES,

1502
00:58:58,976 --> 00:59:01,512
HERE YOU SEE A BROKEN DOWN, THIS

1503
00:59:01,512 --> 00:59:04,449
RICH ACTIVITY INTO 18 TYPES, CAN

1504
00:59:04,449 --> 00:59:06,684
YOU KEEP GOING, AND WE MAP THEM

1505
00:59:06,684 --> 00:59:08,986
ACROSS THE BRAIN, YOU WILL SEE

1506
00:59:08,986 --> 00:59:13,458
THAT THIS ACTIVITY ACTUALLY

1507
00:59:13,458 --> 00:59:14,659
SPANS THE ENTIRETY OF THE BRAIN.

1508
00:59:14,659 --> 00:59:17,495
SO EACH 1 HASSA AUDITORY

1509
00:59:17,495 --> 00:59:20,398
RESPONSES AND IT'S NOT AS IF A

1510
00:59:20,398 --> 00:59:21,799
MONOLITHIC SIGNAL IS PROJECTED

1511
00:59:21,799 --> 00:59:22,967
EVERYWHERE AND THERE'S RICH

1512
00:59:22,967 --> 00:59:24,202
DIVERSE AND DIFFERENT ACTIVITY

1513
00:59:24,202 --> 00:59:26,304
IN EACH OF THESE REGIONS, CAN

1514
00:59:26,304 --> 00:59:29,240
YOU KEEP GOING, EVEN IN REGIONS

1515
00:59:29,240 --> 00:59:31,309
THAT HAVE BEEN STUDIED BY LI AND

1516
00:59:31,309 --> 00:59:36,214
OTHERS IS  PREDOMINANTLY

1517
00:59:36,214 --> 00:59:38,516
OLFACTORY OR INDIVIDUAL, AND

1518
00:59:38,516 --> 00:59:39,450
THIS RESULT PARALLELLED OTHER

1519
00:59:39,450 --> 00:59:41,252
RESULTS THAT WERE COMING OUT AT

1520
00:59:41,252 --> 00:59:43,721
THE TIME FROM FLIES AND MICE IN

1521
00:59:43,721 --> 00:59:46,691
WHICH THE LENS HAD BEEN OPEN

1522
00:59:46,691 --> 00:59:49,060
RECORD ACTIVITY AT BRAIN SCALE

1523
00:59:49,060 --> 00:59:51,162
BUT OF LOCOMOTE OR ACTIVITY AND

1524
00:59:51,162 --> 00:59:52,063
THIS ACTIVITY WAS REALLY

1525
00:59:52,063 --> 00:59:53,364
EVERYWHERE AND THERE'S BEEN

1526
00:59:53,364 --> 00:59:54,732
OTHER RESULTS SIMILARLY.

1527
00:59:54,732 --> 00:59:56,300
SO THE CHALLENGE THEN IS TO

1528
00:59:56,300 --> 01:00:00,605
FIGURE OUT, THAT'S FINE, YOAN

1529
01:00:00,605 --> 01:00:02,607
MONCADAURE GOOD -- THE CHALLENGE

1530
01:00:02,607 --> 01:00:04,409
IS TO FIGURE OUT HOW TO ACTIVITY

1531
01:00:04,409 --> 01:00:05,676
SPREADS THROUGHOUT THE BRAIN BUT

1532
01:00:05,676 --> 01:00:08,045
MORE IMPORTANTLY WHAT IT'S DOING

1533
01:00:08,045 --> 01:00:10,014
THERE, HOW IS THIS BRAIN SCALE

1534
01:00:10,014 --> 01:00:11,082
ACTIVITY ACTUALLY USED BY THE

1535
01:00:11,082 --> 01:00:11,949
BRAIN TO DRIVE BEHAVIOR.

1536
01:00:11,949 --> 01:00:13,384
SO I WILL TELL YOU FIRST ABOUT

1537
01:00:13,384 --> 01:00:16,788
THE BUILDING OF A CONNECTOME IN

1538
01:00:16,788 --> 01:00:17,989
THE DROSOPHILA BRAIN TO COVER

1539
01:00:17,989 --> 01:00:18,790
THIS INFORMATION AT BRAIN SCALE

1540
01:00:18,790 --> 01:00:20,158
AND THEN I WILL TELL YOU A

1541
01:00:20,158 --> 01:00:21,793
LITTLE BIT ABOUT OUR DEVELOPMENT

1542
01:00:21,793 --> 01:00:23,161
OF NEURAL NETWORK MODELS TO

1543
01:00:23,161 --> 01:00:29,167
START TO GET AT THE FUNCTION OF

1544
01:00:29,167 --> 01:00:30,535
THE INDIVIDUAL CELL TYPES OF THE

1545
01:00:30,535 --> 01:00:32,437
BRAIN, SO THIS IS 1 TECHNIQUE TO

1546
01:00:32,437 --> 01:00:38,876
DO THIS SO WE CAN TART TO

1547
01:00:38,876 --> 01:00:40,478
DESCRIBE BRAIN CONNECTOME AND

1548
01:00:40,478 --> 01:00:41,479
KEEP GOING.

1549
01:00:41,479 --> 01:00:43,414
SO OUR PROJECT STARTED WITH A

1550
01:00:43,414 --> 01:00:46,651
DATA SET RELEASED BACK IN 2018

1551
01:00:46,651 --> 01:00:49,954
FROM GENELIA, AND THIS WAS A

1552
01:00:49,954 --> 01:00:52,723
WHOLE BRAIN MICROSCOPIC VOLUME,

1553
01:00:52,723 --> 01:00:54,725
1 FEMALE BRAIN CUT INTO 7000

1554
01:00:54,725 --> 01:00:56,160
SECTIONS AND THEN MILLIONS OF

1555
01:00:56,160 --> 01:00:58,763
IMAGES WITH THE EM SCOPE WERE

1556
01:00:58,763 --> 01:01:00,531
TAKEN AND GENELIA HAD ACTUALLY

1557
01:01:00,531 --> 01:01:02,300
KIND OF ABANDONED THIS DATA SET,

1558
01:01:02,300 --> 01:01:04,602
THEY THOUGHT THEY COULDN'T

1559
01:01:04,602 --> 01:01:08,406
ASSEMBLE IT INTO THE VOLUME AND

1560
01:01:08,406 --> 01:01:09,674
RECONSTRUCT IT AND SO THEY MOVED

1561
01:01:09,674 --> 01:01:11,042
TO TO A DIFFERENT METHOD TO

1562
01:01:11,042 --> 01:01:13,010
COLLECT WHAT IS CALLED THE

1563
01:01:13,010 --> 01:01:14,512
HEMIBRAIN OR A VIRTUAL WIRING

1564
01:01:14,512 --> 01:01:15,513
DIAGRAM OF A DIFFERENT FEMALE

1565
01:01:15,513 --> 01:01:17,982
BSM YOU IT WAS AROUND THAT TIME

1566
01:01:17,982 --> 01:01:18,850
THAT SEBASTIAN JOINED US AT

1567
01:01:18,850 --> 01:01:20,084
PRINCETON AND WE LOOKEDDA THE

1568
01:01:20,084 --> 01:01:21,619
THIS DATA AND WE THOUGHT WE

1569
01:01:21,619 --> 01:01:27,191
COULD ACTUALLY USE THE

1570
01:01:27,191 --> 01:01:28,326
CONVOLUTIONAL NEURONET AND OOH

1571
01:01:28,326 --> 01:01:28,826
PLIE IT TO THE FLY.

1572
01:01:28,826 --> 01:01:33,264
SO THE IDEA WAS THAT WE WOULD

1573
01:01:33,264 --> 01:01:35,233
ACTUAL LEE AUTOMATE THE NEURAL

1574
01:01:35,233 --> 01:01:36,901
CONSTRUCK, WE WOULD REASSEMBLE

1575
01:01:36,901 --> 01:01:38,936
THE VOLUME AND TRAIN HIS NEURAL

1576
01:01:38,936 --> 01:01:40,037
NETWORKS ON FLY GROUND TRUTH

1577
01:01:40,037 --> 01:01:40,371
DATA.

1578
01:01:40,371 --> 01:01:44,775
AND IT WAS AROUND THAT TIME THAT

1579
01:01:44,775 --> 01:01:48,846
SPAN JOINED OUR LAB AND SVEN

1580
01:01:48,846 --> 01:01:51,048
TOOK THIS DATA AND DEVELOPED A

1581
01:01:51,048 --> 01:01:51,816
PROOF READING PLATFORM SO MAYBE

1582
01:01:51,816 --> 01:01:53,184
IF YOU GO TO THE NEXT SLIDE, I

1583
01:01:53,184 --> 01:01:54,318
WILL EXPLAIN WHAT THIS IS.

1584
01:01:54,318 --> 01:01:56,554
SO IN THE TOP ROW HERE, CAN YOU

1585
01:01:56,554 --> 01:01:59,257
SEE WHAT THE AUTOMATED

1586
01:01:59,257 --> 01:02:00,424
RECONSTRUCTIONS FROM THAT EM

1587
01:02:00,424 --> 01:02:03,160
VOLUME WHICH I SHOWED YOU IN THE

1588
01:02:03,160 --> 01:02:04,562
VIDEO IN THE PREVIOUS SLIDE LOOK

1589
01:02:04,562 --> 01:02:04,862
TD LIKE.

1590
01:02:04,862 --> 01:02:07,031
AND IN THE BOTTOM ROW, YOU WILL

1591
01:02:07,031 --> 01:02:07,865
SEE WHAT THOSE RECONSTRUCTIONS

1592
01:02:07,865 --> 01:02:08,699
LOOK LIKE AFTER PROOF READING

1593
01:02:08,699 --> 01:02:10,968
AND I HOPE YOU CAN SEE THERE'S

1594
01:02:10,968 --> 01:02:11,335
DIFFERENCES.

1595
01:02:11,335 --> 01:02:13,538
THERE'S PIECES OF NEURON THAT

1596
01:02:13,538 --> 01:02:14,272
ARE MISSING OR THERE'S EXTRA

1597
01:02:14,272 --> 01:02:15,773
PIECES THAT HAVE TO BE FIXED AND

1598
01:02:15,773 --> 01:02:20,478
YOU KNOW I HAVE A LOT OF

1599
01:02:20,478 --> 01:02:21,679
EXPERIENCE WITH USING NEURAL

1600
01:02:21,679 --> 01:02:23,447
NETWORKING FOR EXAMPLE TO TRACK

1601
01:02:23,447 --> 01:02:24,382
ANIMAL BEHAVIOR, ET CETERA

1602
01:02:24,382 --> 01:02:25,583
SOPHISTICATED THERE WE DON'T DO

1603
01:02:25,583 --> 01:02:26,384
PROOF READING.

1604
01:02:26,384 --> 01:02:28,286
WE COLLECT HUGE DATA SETS AND WE

1605
01:02:28,286 --> 01:02:29,053
LET THE AUTOMATED METHODDINGS DO

1606
01:02:29,053 --> 01:02:32,156
THEIR WORK AND THEN, WE -- YOU

1607
01:02:32,156 --> 01:02:33,691
KNOW WE KIND OF AVERAGE OVER

1608
01:02:33,691 --> 01:02:35,326
THIS NOISE BUT IN CONNECT ORDER

1609
01:02:35,326 --> 01:02:36,827
OF MICRONSICS YOU CANNOT DO

1610
01:02:36,827 --> 01:02:39,730
THAT, IT'S AN NOF 1 AND THESE

1611
01:02:39,730 --> 01:02:41,465
ERRORS PROPAGATE THROUGH THE

1612
01:02:41,465 --> 01:02:42,800
ENTIRE NETWORK, THEY HAVE TO BE

1613
01:02:42,800 --> 01:02:44,168
FIXED SO IN THE RIGHT, YOU CAN

1614
01:02:44,168 --> 01:02:47,572
SEE A NEURON AND IA NUMBER OF

1615
01:02:47,572 --> 01:02:49,373
DIFFERENT USERS OF THIS PROOF

1616
01:02:49,373 --> 01:02:50,808
READING PLATFORM, MOST OF EDITS

1617
01:02:50,808 --> 01:02:52,810
THOSE ARE THE BLACK DOTS THAT

1618
01:02:52,810 --> 01:02:54,111
ARE HAPPENING WITHIN THE

1619
01:02:54,111 --> 01:02:55,713
DENDRITIC TREE BUT THERE ARE

1620
01:02:55,713 --> 01:02:58,282
EDITS THAT HAPPEN ELSEWHERE AND

1621
01:02:58,282 --> 01:03:03,120
SO SVEN DEVELOPED A SYSTEM THAT

1622
01:03:03,120 --> 01:03:04,722
WAS VERY STRAIGHT FORWARD TO

1623
01:03:04,722 --> 01:03:07,058
USE, VERY FAST, LARGE NUMBERS OF

1624
01:03:07,058 --> 01:03:07,858
PEOPLE COLLABORATING TOGETHER.

1625
01:03:07,858 --> 01:03:09,493
SO IN THE MOVIE YOU WILL SEE

1626
01:03:09,493 --> 01:03:19,236
THESE EDITS ARE PILING UP IN THE

1627
01:03:19,236 --> 01:03:20,438
GATTA  -- DATA SET.

1628
01:03:20,438 --> 01:03:23,374
AND SO WE OPENED IT UP TO THE

1629
01:03:23,374 --> 01:03:25,509
ENTIRE COMMUNITY AS SOON AS WE

1630
01:03:25,509 --> 01:03:27,678
HAD THIS, BUT WE DID IT TO HELP

1631
01:03:27,678 --> 01:03:29,080
WITH THE CONNECTOME BUT WE ALSO

1632
01:03:29,080 --> 01:03:31,182
KNEW IT WOULD DRIVE THE SCIENCE

1633
01:03:31,182 --> 01:03:32,984
AND IT DID AS I WILL EXPLAIN.

1634
01:03:32,984 --> 01:03:33,684
SOPHISTICATEDY THAT'S PROOF

1635
01:03:33,684 --> 01:03:35,186
READING AND THAT'S WHAT TOOK THE

1636
01:03:35,186 --> 01:03:38,255
BULK OF TIME AND I'M TELLING ALL

1637
01:03:38,255 --> 01:03:40,024
THAT BECAUSE CONNECT OHMICS IS A

1638
01:03:40,024 --> 01:03:42,660
MAJOR PART OF THE RESEARCH

1639
01:03:42,660 --> 01:03:44,595
PORTFOLIO, I THINK IT'S VALUABLE

1640
01:03:44,595 --> 01:03:46,297
TO KNOW HOW THESE DATA SETS ARE

1641
01:03:46,297 --> 01:03:46,597
GENERATED.

1642
01:03:46,597 --> 01:03:47,765
IF YOU GO FORWARD, THERE'S 2

1643
01:03:47,765 --> 01:03:50,101
OTHER PIECES THAT HAVE TO BE

1644
01:03:50,101 --> 01:03:52,036
COMPLETED FOR A COMNECT OHM, 1

1645
01:03:52,036 --> 01:03:53,571
IS AUTOMATED SYNAPSE DETECTION,

1646
01:03:53,571 --> 01:03:55,339
YOU CAN SEE THAT IN THE LOWER

1647
01:03:55,339 --> 01:03:56,741
LEFT HAND CORNER.

1648
01:03:56,741 --> 01:03:59,210
WE DON'T PROOF READ THOSE

1649
01:03:59,210 --> 01:04:00,578
DETECTIONS SO WE USE THE

1650
01:04:00,578 --> 01:04:01,612
AUTOMATED DETECTION AND THAT'S

1651
01:04:01,612 --> 01:04:04,281
IN PART BECAUSE AT LEAST IN

1652
01:04:04,281 --> 01:04:05,616
DROSOPHILA WHEN 2 NEURONS

1653
01:04:05,616 --> 01:04:08,252
CONNECT, THEY DO SO EAEVER MANY

1654
01:04:08,252 --> 01:04:09,620
SYNAPTIC CONNECTIONS SO WE CAN

1655
01:04:09,620 --> 01:04:11,389
TOLERATE A LITTLE BIT OF ERROR

1656
01:04:11,389 --> 01:04:13,157
THERE, ALTHOUGH THE DETECTION IS

1657
01:04:13,157 --> 01:04:14,492
QUITE GOOD AND WE ALSO HAVE IN

1658
01:04:14,492 --> 01:04:19,330
OUR DATA SET, THIS IS WORK DONE

1659
01:04:19,330 --> 01:04:23,701
BY YAN AND LI CHEN'S FORMER

1660
01:04:23,701 --> 01:04:24,835
GRADUATE STUDENT, SO WE COULD

1661
01:04:24,835 --> 01:04:27,838
TELL BY LOOKING AT THE IMAGES

1662
01:04:27,838 --> 01:04:28,606
WHAT NEUROTRANSMITTER EACH 1

1663
01:04:28,606 --> 01:04:30,207
EXPRESSES AND BOTH OF THESE

1664
01:04:30,207 --> 01:04:32,677
PIECES OF INFORMATION ARE

1665
01:04:32,677 --> 01:04:33,744
ESENATIAL FOR THE CONNECTOME.

1666
01:04:33,744 --> 01:04:34,612
CAN YOU GO FORWARD.

1667
01:04:34,612 --> 01:04:36,947
THESE ARE THE MEMBERS OF THE FLY

1668
01:04:36,947 --> 01:04:38,182
WIRE CONSORTIUM, THEY CAME FROM

1669
01:04:38,182 --> 01:04:40,384
ALL OVER THE WORLD HELPING US TO

1670
01:04:40,384 --> 01:04:45,122
PROOF READ AND ANNOTATE THE

1671
01:04:45,122 --> 01:04:45,456
NEURONS.

1672
01:04:45,456 --> 01:04:46,957
AND AND IF YOU GO FOR, YOU WILL

1673
01:04:46,957 --> 01:04:49,160
SEE THE TOP OF THE LIST SO WE

1674
01:04:49,160 --> 01:04:50,795
KEEP TRACK OF ALL THE PAPERS

1675
01:04:50,795 --> 01:04:52,797
THAT USE FLY WIRE ON OUR WEBSITE

1676
01:04:52,797 --> 01:04:59,570
AND THIS LIST IS JUST GROWING

1677
01:04:59,570 --> 01:05:01,472
WEEK BY WEEK BUT YOU CAN SEE

1678
01:05:01,472 --> 01:05:02,807
BEFORE THIS PUBLICATION CAME OUT

1679
01:05:02,807 --> 01:05:04,775
THERE WERE LOTS OF PAPERS WHO

1680
01:05:04,775 --> 01:05:06,911
WERE USING THE DATA SO IT REALLY

1681
01:05:06,911 --> 01:05:08,079
DID PUSH PEOPLE WITH THE

1682
01:05:08,079 --> 01:05:08,713
SCIENTIFIC DISCOVERIES WITH THE

1683
01:05:08,713 --> 01:05:09,046
DATA.

1684
01:05:09,046 --> 01:05:10,981
SO I GUESS AS THE MOVIE PLAYS, I

1685
01:05:10,981 --> 01:05:12,983
WILL SAY 1 MORE WORD ABOUT DATA

1686
01:05:12,983 --> 01:05:14,251
SHARING, I THINK SORT OF THE

1687
01:05:14,251 --> 01:05:19,690
TRADITION FOR US IS TO KIND OF

1688
01:05:19,690 --> 01:05:21,826
CAREFULLY CURATE THE DATA AND AT

1689
01:05:21,826 --> 01:05:22,927
PUBLICATION TIME SHARE IT AND I

1690
01:05:22,927 --> 01:05:26,430
THINK IT WORKS WELL IF A LOT OF

1691
01:05:26,430 --> 01:05:27,465
CASES BUT FOR CONNECTOMICS IN A

1692
01:05:27,465 --> 01:05:29,333
LOT OF CASES, THERE IS A LOT OF

1693
01:05:29,333 --> 01:05:30,034
SCIENCE THAT HAS TO BE DONE

1694
01:05:30,034 --> 01:05:31,302
BEFORE THE DAILY BASIS THEA SET

1695
01:05:31,302 --> 01:05:33,971
IS COMPLETED AND MY OWN OPINION

1696
01:05:33,971 --> 01:05:35,339
IS THAT IT'S IMPORTANT TO SHARE

1697
01:05:35,339 --> 01:05:36,907
THIS DATA AS EARLY AS POSSIBLE.

1698
01:05:36,907 --> 01:05:39,043
SO AS JOHN MENTIONED THERE'S

1699
01:05:39,043 --> 01:05:40,177
ALMOST 140,000 NEURONS IN THIS

1700
01:05:40,177 --> 01:05:42,847
CONNECT OHM AND YOU WILL SEE THE

1701
01:05:42,847 --> 01:05:44,081
VARIOUS TYPES HERE.

1702
01:05:44,081 --> 01:05:46,150
THEIR CONNECTED BY OVER

1703
01:05:46,150 --> 01:05:46,917
50 MILLION SYNAPTIC CONNECTIONS

1704
01:05:46,917 --> 01:05:50,855
AND WE CAN COMPRESS THESE

1705
01:05:50,855 --> 01:05:51,989
NEURONS INTO ABOUT 8500 TYPES.

1706
01:05:51,989 --> 01:05:53,791
SO I WON'T PLAY THIS WHOLE VIDEO

1707
01:05:53,791 --> 01:06:00,297
BECAUSE YOU'VE HEARD SEEN IT, WE

1708
01:06:00,297 --> 01:06:00,664
CAN KEEP GOING.

1709
01:06:00,664 --> 01:06:02,433
AND I WILL EXPLAIN WHAT THOSE

1710
01:06:02,433 --> 01:06:04,135
CELL TYPES ARE IN JUST A MOMENT

1711
01:06:04,135 --> 01:06:05,603
BUT BEFORE I DO, THE DAILY BASIS

1712
01:06:05,603 --> 01:06:07,905
THEA HAS BEEN SHARED VIA

1713
01:06:07,905 --> 01:06:11,175
PLATFORM BECAUSE ALL CODE X,

1714
01:06:11,175 --> 01:06:17,782
CONNECTING THOSE AND THE BRAIN

1715
01:06:17,782 --> 01:06:26,290
CHILD OF OUR SOFTWARE ENGINEERS

1716
01:06:26,290 --> 01:06:26,924
AND CODEX, IS REALLY GREAT, CAN

1717
01:06:26,924 --> 01:06:29,460
YOU USE IT TO MAKE DISCOVERIES

1718
01:06:29,460 --> 01:06:30,828
USING THE DATA AND USING IT TO

1719
01:06:30,828 --> 01:06:34,498
GO FORWARD, THIS IS A MAP OF

1720
01:06:34,498 --> 01:06:39,203
WHERE AT LEAST 10 SEARCHES HAVE

1721
01:06:39,203 --> 01:06:41,138
OCCURRED IN CODEX, AND YOU CAN

1722
01:06:41,138 --> 01:06:43,374
SEE THEY TILE THE GLOBE, IF YOU

1723
01:06:43,374 --> 01:06:47,311
ZOOM IN, THESE DOTS ARE NOT JUST

1724
01:06:47,311 --> 01:06:49,113
AT UNIVERSITIES, THEY'RE

1725
01:06:49,113 --> 01:06:52,049
ACTUALLY ALL OVER THE PLACE, YOU

1726
01:06:52,049 --> 01:06:53,617
KNOW DISTRIBUTED, I'M CURIOUS TO

1727
01:06:53,617 --> 01:06:55,820
KNOW WHAT PEOPLE ARE USING FLY

1728
01:06:55,820 --> 01:06:58,389
WIRE FOR AND IT'S VERY EXCITING.

1729
01:06:58,389 --> 01:07:00,791
THIS BOTTOM PLOT IS A CODEX OF

1730
01:07:00,791 --> 01:07:03,494
USERS AND THIS IS DAILY

1731
01:07:03,494 --> 01:07:05,729
ACTIVITY SO YOU CAN SEE WE HAVE

1732
01:07:05,729 --> 01:07:09,900
SEVERAL THOUSAND USERS IN CODEX,

1733
01:07:09,900 --> 01:07:11,202
AND THIS DISSEMINATION AND

1734
01:07:11,202 --> 01:07:14,538
FUNDED RO21 AND THESE ARE PRETTY

1735
01:07:14,538 --> 01:07:16,707
GOOD NUMBERS FOR DISSEMINATION

1736
01:07:16,707 --> 01:07:17,341
OF BRAIN INITIATIVE AND WE'RE

1737
01:07:17,341 --> 01:07:20,110
PROUD OF THIS AND THANKS A LOT

1738
01:07:20,110 --> 01:07:21,812
TO ARIE FOR HIS SKILL IN SHARING

1739
01:07:21,812 --> 01:07:26,884
THE DAILY BASIS THEA. 

1740
01:07:26,884 --> 01:07:27,017
D

1741
01:07:27,017 --> 01:07:27,151
A

1742
01:07:27,151 --> 01:07:27,284
T

1743
01:07:27,284 --> 01:07:27,418
A

1744
01:07:27,418 --> 01:07:27,551
.

1745
01:07:27,551 --> 01:07:28,719
OKAY, CAN YOU KEEP GOING.

1746
01:07:28,719 --> 01:07:30,754
SO OUR EFFORT HASN'T ENDED

1747
01:07:30,754 --> 01:07:32,256
THERE, WE'RE ADDING ARK DITIONAL

1748
01:07:32,256 --> 01:07:36,927
DATA SETS, WE'RE NOT ONLY WITH

1749
01:07:36,927 --> 01:07:39,296
CONNECTOME, BUT CONNECTOMES WITH

1750
01:07:39,296 --> 01:07:41,098
DROSOPHILA AND WE'VE BEEN

1751
01:07:41,098 --> 01:07:44,802
TEAMING UP WITH LI'S LAB TO

1752
01:07:44,802 --> 01:07:46,637
STUDY THE FINE SALIVA--SALIVA

1753
01:07:46,637 --> 01:07:48,038
CORD FOR DROSOPHILA THAT

1754
01:07:48,038 --> 01:07:48,839
MEDIATES THE MOTOR FUNCTIONS AND

1755
01:07:48,839 --> 01:07:51,942
YOU CAN SEE HERE A SET OF

1756
01:07:51,942 --> 01:07:53,444
ASCENDING AND DESCENDING NEURONS

1757
01:07:53,444 --> 01:07:55,279
THAT TRAVEL THROUGH THE NECK AND

1758
01:07:55,279 --> 01:07:57,047
WE'RE NEARING COMPLETION ON THIS

1759
01:07:57,047 --> 01:07:58,749
DATA SET AS YOU SEE AND I THINK

1760
01:07:58,749 --> 01:08:00,451
YOU WILL SEE OTHER DATA SETS

1761
01:08:00,451 --> 01:08:02,253
COMING OUT NOT ONLY FROM OUR

1762
01:08:02,253 --> 01:08:06,156
GROUP BUT GROUP AS THE GENELIA

1763
01:08:06,156 --> 01:08:07,658
ET CETERA AND WE'RE GETTING INTO

1764
01:08:07,658 --> 01:08:08,826
A PLACE FOR NEUROSCIENCE WHERE

1765
01:08:08,826 --> 01:08:10,160
WE THINK ABOUT INDIVIDUAL

1766
01:08:10,160 --> 01:08:11,862
VARIABILITY, WE THINK ABOUT

1767
01:08:11,862 --> 01:08:13,430
SPECIES VARIABILITY, ET CETERA,

1768
01:08:13,430 --> 01:08:13,898
IT'S QUITE EXCITING.

1769
01:08:13,898 --> 01:08:15,299
ALL RIGHT, WELL THE'S KEEP GOING

1770
01:08:15,299 --> 01:08:16,233
EMPLOY ALL RIGHT, SO I WANT TO

1771
01:08:16,233 --> 01:08:19,370
JUST TELL YOU A LITTLE BIT ABOUT

1772
01:08:19,370 --> 01:08:20,404
THE STATISTICS OF THE CONNECT

1773
01:08:20,404 --> 01:08:21,605
OHM AND JOHN IF WE GO OVER,

1774
01:08:21,605 --> 01:08:23,007
JUDGE UOF THE CUT ME OFF

1775
01:08:23,007 --> 01:08:24,909
WHENEVER YOU WANT, I AM HAPPY TO

1776
01:08:24,909 --> 01:08:26,010
STOP TALKING AT ANY POINT.

1777
01:08:26,010 --> 01:08:28,379
IF PEOPLE WANT TO INTERRUPT WITH

1778
01:08:28,379 --> 01:08:30,814
QUESTIONS THAT'S ALSO PERFECTLY

1779
01:08:30,814 --> 01:08:32,116
OKAY NWE'RE GOOD, WE'RE ALL

1780
01:08:32,116 --> 01:08:37,888
GOOD, WE'RE ALL ENJOYING THIS.

1781
01:08:37,888 --> 01:08:38,289
>> OKAY.

1782
01:08:38,289 --> 01:08:41,158
ALL RIGHT SO 1 OF THE PAPERS OF

1783
01:08:41,158 --> 01:08:44,395
THE CONNECTOME PACKAGE IS WE

1784
01:08:44,395 --> 01:08:45,996
WORKED ON THE ASPECTS AND 1

1785
01:08:45,996 --> 01:08:48,599
PROPERTY IS THAT IT'S A SINGLE

1786
01:08:48,599 --> 01:08:49,767
CONNECTED COMPONENT SO ABOUT 93%

1787
01:08:49,767 --> 01:08:52,903
OF ALL THE NEURONS IN THIS BRAIN

1788
01:08:52,903 --> 01:08:54,872
LIVE IN THIS 1 COMPONENT AND IN

1789
01:08:54,872 --> 01:08:56,240
THAT COMPONENT IF I START TO 1

1790
01:08:56,240 --> 01:08:58,509
NEURON I CAN GET TO ANY OTHER

1791
01:08:58,509 --> 01:08:59,910
NEURONS WITHIN 4 OR 5 HOPS.

1792
01:08:59,910 --> 01:09:02,780
SO THIS SAYS THAT THE NETWORK IS

1793
01:09:02,780 --> 01:09:04,348
HIGHLY CONNECTIVE, BUT IF YOU

1794
01:09:04,348 --> 01:09:05,950
KEEP GOING, THE FLY BRAIN IS

1795
01:09:05,950 --> 01:09:07,851
SPARSE SO THE PROBABILITY THAT

1796
01:09:07,851 --> 01:09:12,089
ANY 2 NEURONS ARE CONNECT SIDE

1797
01:09:12,089 --> 01:09:14,491
.02%, SO LOW, MUCH LORER THAN

1798
01:09:14,491 --> 01:09:16,293
C.ELEGANS FOR EXAMPLE BUT IT'S

1799
01:09:16,293 --> 01:09:17,962
HIGHLY RECURRENT SO IF A

1800
01:09:17,962 --> 01:09:20,097
CONNECTS TO B, THE PROBABILITY

1801
01:09:20,097 --> 01:09:24,868
THAT B CONNECTS BACK TO A IS

1802
01:09:24,868 --> 01:09:25,302
14%.

1803
01:09:25,302 --> 01:09:26,337
AND IN THE LAST PART OF THE

1804
01:09:26,337 --> 01:09:29,006
SLIDE, YOU WILL SEE THAT THE FLY

1805
01:09:29,006 --> 01:09:30,507
BRAIN HAS THIS VERY LARGE PART

1806
01:09:30,507 --> 01:09:31,809
OF NEURONS, THESE NEURONS ARE

1807
01:09:31,809 --> 01:09:33,811
NOT ONLY CONNECKED TO THE NEURON

1808
01:09:33,811 --> 01:09:34,545
BUT THEY'RE HIGHLY CONNECTED TO

1809
01:09:34,545 --> 01:09:37,748
EACH OTHER AND THIS IS AN

1810
01:09:37,748 --> 01:09:38,248
EXTREMELY LARGE NUMBER

1811
01:09:38,248 --> 01:09:39,416
RESPONSIBLE FOR THIS PROPERTY.

1812
01:09:39,416 --> 01:09:41,618
WHICH MEANS YOU CAN'T JUST PRUNE

1813
01:09:41,618 --> 01:09:43,620
AWAY 1 NEURON AND HAVE THE

1814
01:09:43,620 --> 01:09:45,289
NETWORK FALL APART, THERE'S

1815
01:09:45,289 --> 01:09:47,758
THOUSANDS OF THESE HUB NEURONS

1816
01:09:47,758 --> 01:09:48,926
THAT ARE CONNECTING EVERYTHING

1817
01:09:48,926 --> 01:09:49,360
TOGETHER.

1818
01:09:49,360 --> 01:09:50,160
WE CAN KEEP GOING.

1819
01:09:50,160 --> 01:09:52,162
BECAUSE OF THIS PROPERTY, IF WE

1820
01:09:52,162 --> 01:09:53,197
LOOK AT INFORMATION FLOW IN THE

1821
01:09:53,197 --> 01:09:55,032
CONNECT OHM, WE TART WITH THE

1822
01:09:55,032 --> 01:09:56,500
SENSORY RECEPTOR POPULATION,

1823
01:09:56,500 --> 01:09:57,868
LIKE THE TASTE NEURONS AND THEN

1824
01:09:57,868 --> 01:10:00,637
WE FOLLOW INFORMATION FLOW,

1825
01:10:00,637 --> 01:10:01,271
PROBABLISTICALLY THROUGHOUT THE

1826
01:10:01,271 --> 01:10:03,073
NETWORK WE CAN MAKE A MAP, HERE

1827
01:10:03,073 --> 01:10:05,342
YOU SEE A UMAP WHERE EACH DOT IN

1828
01:10:05,342 --> 01:10:08,512
THE UMAP IS A NEURON.

1829
01:10:08,512 --> 01:10:11,081
AND IT'S POSITION IN THE UMAP IS

1830
01:10:11,081 --> 01:10:13,017
ITS PROXIMITY TO ALL OF THE

1831
01:10:13,017 --> 01:10:13,884
DIFFERENT SENSORY RECEPTOR

1832
01:10:13,884 --> 01:10:14,118
NEURONS.

1833
01:10:14,118 --> 01:10:15,519
SO CAN YOU SEE AT THE BOTTOM

1834
01:10:15,519 --> 01:10:16,754
HERE, THE NEURONS THAT ARE

1835
01:10:16,754 --> 01:10:18,989
COLORED RED ARE VERY CLOSE TO

1836
01:10:18,989 --> 01:10:20,424
THE TASTE RECEPTORS AND THE

1837
01:10:20,424 --> 01:10:22,259
NEURONS THAT ARE BLUE ARE

1838
01:10:22,259 --> 01:10:22,860
FURTHER AWAY.

1839
01:10:22,860 --> 01:10:26,997
AND NEURONS CLUSTER VERY NICELY

1840
01:10:26,997 --> 01:10:28,165
IN THIS SPACE, BUT IF YOU GO

1841
01:10:28,165 --> 01:10:29,566
FORWARD, YOU WILL SEE THAT IF WE

1842
01:10:29,566 --> 01:10:31,301
LOOK THEA DIFFERENT SENSORY

1843
01:10:31,301 --> 01:10:37,374
RECEPTOR POOLS, THERE'S LOTS OF

1844
01:10:37,374 --> 01:10:37,674
OVERLAP.

1845
01:10:37,674 --> 01:10:41,779
SO IT DOESN'T AS IF NEURONS ARE

1846
01:10:41,779 --> 01:10:43,313
SEGREGATED, THEY'RE REALLY

1847
01:10:43,313 --> 01:10:45,382
HIGHLY MIXED IN THIS

1848
01:10:45,382 --> 01:10:47,317
MULTISENSORY MILIEU, AND THIS

1849
01:10:47,317 --> 01:10:48,318
PARALLELS THE IMAGING RESULTS I

1850
01:10:48,318 --> 01:10:50,120
TOLD YOU ABOUT BEFORE BUT IT

1851
01:10:50,120 --> 01:10:53,524
PRESENTS CHALLENGES TO THINK

1852
01:10:53,524 --> 01:10:54,725
ABOUT HOW TO INTERPRET THESE

1853
01:10:54,725 --> 01:10:57,027
NEURONS IN TERMS OF THEIR

1854
01:10:57,027 --> 01:10:58,062
SENSORY CODING AND THE

1855
01:10:58,062 --> 01:10:58,962
INFORMATION THEY CARRY.

1856
01:10:58,962 --> 01:11:06,070
OKAY, LET'S GO FORWARD.

1857
01:11:06,070 --> 01:11:08,005
OKAY BUT IT'S NOT A MESS AND

1858
01:11:08,005 --> 01:11:09,306
THERE'S A WEIGH TO FIND LOGIC

1859
01:11:09,306 --> 01:11:10,774
THROUGH THIS AND THAT COMES IN

1860
01:11:10,774 --> 01:11:13,510
CELL TYPING MUCH OUR FIRST FORAY

1861
01:11:13,510 --> 01:11:15,979
INTO THIS WAS A EFFORT LED BY

1862
01:11:15,979 --> 01:11:18,182
GREG JEFFERS AND HE DECIDED TO

1863
01:11:18,182 --> 01:11:20,350
TAKE THE LABELS OF THE CELLS

1864
01:11:20,350 --> 01:11:21,351
THAT PRODUCED BY THE HEMIBRAIN

1865
01:11:21,351 --> 01:11:25,055
PROJECT THAT CAME OUT OF

1866
01:11:25,055 --> 01:11:25,956
GENELIA, SO THEY FOUND 5000 CELL

1867
01:11:25,956 --> 01:11:29,159
TYPES WITHIN A SMALLER VOLUME OF

1868
01:11:29,159 --> 01:11:31,161
ABOUT 22,000 NEURONS AND SO GREG

1869
01:11:31,161 --> 01:11:32,896
MATCHED THESE NEURONS TO OUR

1870
01:11:32,896 --> 01:11:34,331
DATA SET USING MORPHOLOGY AND

1871
01:11:34,331 --> 01:11:36,133
HERE YOU CAN SEE 1 EXAMPLE OF A

1872
01:11:36,133 --> 01:11:37,868
NEURON FOR THE HEMIBRAIN AND

1873
01:11:37,868 --> 01:11:41,138
IT'S 2 PARTNERS IN THE LEFT AND

1874
01:11:41,138 --> 01:11:46,143
RIGHT HEMISPHERE IN FLY WIRE, SO

1875
01:11:46,143 --> 01:11:46,777
THERE'S MULTIINDIVIDUAL NEURONS

1876
01:11:46,777 --> 01:11:47,578
IN THE CELL TYPE.

1877
01:11:47,578 --> 01:11:48,946
AND THEN IF YOU GO FORWARD, WE

1878
01:11:48,946 --> 01:11:51,849
WILL SEE THAT WE CAN ACTUALLY

1879
01:11:51,849 --> 01:11:55,853
ASK THE QUESTION, NOW THAT WE

1880
01:11:55,853 --> 01:11:58,188
MATCHED THESE NEURONS HOW

1881
01:11:58,188 --> 01:11:59,623
SIMILAR IS THE CONNECTIVITY.

1882
01:11:59,623 --> 01:12:02,159
SO HERE GREG'S LAB PLOTTED THE

1883
01:12:02,159 --> 01:12:05,362
CONNECTIVITY SIMILARITY WITHIN

1884
01:12:05,362 --> 01:12:06,797
BRAINS COMPARING TO 2 HEMISPHERE

1885
01:12:06,797 --> 01:12:09,666
SPHERES OF FLY WIRE AND ACROSS

1886
01:12:09,666 --> 01:12:10,968
BRAIN COMPARING HEMITO FLY WIRE,

1887
01:12:10,968 --> 01:12:11,869
IT'S ALL HIGHLY SIMILAR WHICH IS

1888
01:12:11,869 --> 01:12:14,771
1 OF THE REASONS WE CAN -- WE

1889
01:12:14,771 --> 01:12:16,573
DON'T HAVE TO DO WHAT WAS DONE

1890
01:12:16,573 --> 01:12:18,809
FOR MIRROR IMAGIONS.

1891
01:12:18,809 --> 01:12:20,344
SO IN MICRONS, THE IMAGE

1892
01:12:20,344 --> 01:12:21,645
RESPONSES AND THEN BUILT A

1893
01:12:21,645 --> 01:12:21,945
CONNECTOME.

1894
01:12:21,945 --> 01:12:24,848
IN THE FLY WE CAN TAKE DATA FROM

1895
01:12:24,848 --> 01:12:28,118
1 FLY AND RELATE IT TO THE

1896
01:12:28,118 --> 01:12:29,319
CONNECTOME OF ANOTHER FLY SO

1897
01:12:29,319 --> 01:12:30,420
IT'S VERY USEFUL IN THE SYSTEM

1898
01:12:30,420 --> 01:12:31,922
AS LONG AS YOU KNOW THE CELL

1899
01:12:31,922 --> 01:12:32,189
TYPES.

1900
01:12:32,189 --> 01:12:34,825
BUT WHAT I HOPE CAN YOU ALSO SEE

1901
01:12:34,825 --> 01:12:36,527
IS THAT THERE'S LESS SIMILARITY

1902
01:12:36,527 --> 01:12:37,828
OF CROSS BRAIN WITHIN THE BRAIN.

1903
01:12:37,828 --> 01:12:38,829
SO THERE IS INDIVIDUAL

1904
01:12:38,829 --> 01:12:40,230
VARIABILITY AND WE WILL HAVE TO

1905
01:12:40,230 --> 01:12:42,199
CONTEND WITH THAT GOING FORWARD

1906
01:12:42,199 --> 01:12:44,168
AS WE THINK ABOUT REALLY HOW TO

1907
01:12:44,168 --> 01:12:49,339
INTERPRET THESE CONNECTOMES.

1908
01:12:49,339 --> 01:12:51,074
SO THE OTHER WHY TO DO CELL

1909
01:12:51,074 --> 01:12:53,377
TYPES IS NOT BY MORPHOLOGY

1910
01:12:53,377 --> 01:12:55,879
MATCHING BUT CONTRADICTLY BY

1911
01:12:55,879 --> 01:12:58,782
CONNECTIVITY AND YOU CAN MAYBE

1912
01:12:58,782 --> 01:13:00,484
CLICK FORWARD, TWICE LI CHEN.

1913
01:13:00,484 --> 01:13:03,787
SO THE EYE CONTAINS THESE

1914
01:13:03,787 --> 01:13:06,523
REPEATING COLUMNS, AND THERE'S

1915
01:13:06,523 --> 01:13:07,491
REPEATING CONNECTIVITY, ALL

1916
01:13:07,491 --> 01:13:12,129
BEHIND THE FACETS OF THE EYE,

1917
01:13:12,129 --> 01:13:13,530
THE 800, AND THIS ALLOWS US TO

1918
01:13:13,530 --> 01:13:15,232
BREAK DOWN THE 43,000 NEURONS

1919
01:13:15,232 --> 01:13:17,968
AND 1 OPTIC LOBE INTO JUST 2030

1920
01:13:17,968 --> 01:13:18,402
TYPES.

1921
01:13:18,402 --> 01:13:20,804
AND SO CAN YOU SEE 2 EXAMPLES

1922
01:13:20,804 --> 01:13:22,839
HERE, THERE'S ABOUT 800 OF THESE

1923
01:13:22,839 --> 01:13:25,375
TM1 NEURONS BUT THERE'S FAR

1924
01:13:25,375 --> 01:13:27,744
FEWER OF THE DM3 NEURONS BECAUSE

1925
01:13:27,744 --> 01:13:29,746
THEY'RE TANGENTIAL AND THEY GO

1926
01:13:29,746 --> 01:13:30,948
ACROSS COLUMNS, SO THIS CELL

1927
01:13:30,948 --> 01:13:33,784
TYPES WAS DONE ONLY BY

1928
01:13:33,784 --> 01:13:35,319
CONNECTIVITY, NOT USING ANY

1929
01:13:35,319 --> 01:13:37,487
TRANSCRIPTIONAL INFORMATION, NO

1930
01:13:37,487 --> 01:13:38,655
MORPHOLOGY, O THIS IS ALSO A

1931
01:13:38,655 --> 01:13:40,857
PATH FORWARD FOR NEW DATA SETS

1932
01:13:40,857 --> 01:13:42,259
USING THE KIND OF TOOLS WE

1933
01:13:42,259 --> 01:13:43,327
DEVELOPED HERE FOR CELL TYPES.

1934
01:13:43,327 --> 01:13:45,195
YOU CAN GO FORWARD.

1935
01:13:45,195 --> 01:13:46,930
AND THEN, THE LAST THING I

1936
01:13:46,930 --> 01:13:49,499
WANTED TO MENTION IN THE REALM

1937
01:13:49,499 --> 01:13:52,002
OF CELL TYPING IS THAT MY LAB

1938
01:13:52,002 --> 01:13:57,474
ALSO LED AN EFFORT TO TAKE LIGHT

1939
01:13:57,474 --> 01:14:01,545
MICROSCOPIC IMAGES OF NEURONS WE

1940
01:14:01,545 --> 01:14:03,313
KNEW WERE SEXUALLY DISCIPLINARY

1941
01:14:03,313 --> 01:14:05,148
MORPHIC, IN FRUITLESS AND DOUBLE

1942
01:14:05,148 --> 01:14:06,917
SECTIONAL ANALYSISS, THESE WERE

1943
01:14:06,917 --> 01:14:09,987
LIKE THE ANDROGEN RECEPTOR OR

1944
01:14:09,987 --> 01:14:13,490
ESTROGEN RECEPTOR IN MAMMALS AND

1945
01:14:13,490 --> 01:14:14,958
HERE WE'RE FINDING THE NEURONS

1946
01:14:14,958 --> 01:14:18,061
IN THE EM DATA SET THAT MATCH

1947
01:14:18,061 --> 01:14:20,564
THIS MORPHOLOGY. 

1948
01:14:20,564 --> 01:14:24,268
BUT WHAT'S NICE BECAUSE WE HAVE

1949
01:14:24,268 --> 01:14:26,103
THE EXHAUSTIVE CELL TYPING

1950
01:14:26,103 --> 01:14:28,005
TECHNOLOGY CAN IS WE CAN SPLIT

1951
01:14:28,005 --> 01:14:30,841
THEM FURTHER AND MOW WE MAKE

1952
01:14:30,841 --> 01:14:33,010
THIS NETWORK OF 1400 NEURONS WE

1953
01:14:33,010 --> 01:14:34,678
KNOW, OR WE THINK THEY DO BASED

1954
01:14:34,678 --> 01:14:36,647
ON MATCHING AND IN THE PLOT AT

1955
01:14:36,647 --> 01:14:38,415
THE LOWER LEFT, YOU CAN SEE WHAT

1956
01:14:38,415 --> 01:14:40,984
THE FRACTION OF INPUT AND OUTPUT

1957
01:14:40,984 --> 01:14:43,587
SYNAPSES IN WITHIN THIS NETWORK,

1958
01:14:43,587 --> 01:14:45,355
THAT'S THAT 1 BLACK DOT AND IT'S

1959
01:14:45,355 --> 01:14:45,722
ABOUT 12%.

1960
01:14:45,722 --> 01:14:47,491
SO THIS IS NOT A PRIVATE NETWORK

1961
01:14:47,491 --> 01:14:49,026
OVER 80% OF THE SYNAPSES COME

1962
01:14:49,026 --> 01:14:52,462
FROM NEURONS THAT ARE NOT

1963
01:14:52,462 --> 01:14:54,064
EXPRESSING FRUITLESS OR DOUBLE

1964
01:14:54,064 --> 01:14:55,465
SEX, BUT IT'S HIGHLY

1965
01:14:55,465 --> 01:14:57,534
INTERCONNECTED SO IF YOU COMPARE

1966
01:14:57,534 --> 01:14:59,903
TO MASH NETWORKS THAT HAVE THE

1967
01:14:59,903 --> 01:15:01,204
SAME SPATIAL DISTRIBUTION BUT

1968
01:15:01,204 --> 01:15:03,173
DON'T EXPRESS THESE

1969
01:15:03,173 --> 01:15:04,908
TRANSCRIPTION FACTORS THE

1970
01:15:04,908 --> 01:15:06,476
INPUT-OUTPUT PERCENTAGES ARE

1971
01:15:06,476 --> 01:15:07,277
MUCH, MUCH LOWER.

1972
01:15:07,277 --> 01:15:08,779
SO THIS IS A VERY INTERESTING

1973
01:15:08,779 --> 01:15:10,314
SET OF NEURONS.

1974
01:15:10,314 --> 01:15:11,548
WE KNOW THEY MUST HAVE

1975
01:15:11,548 --> 01:15:12,683
FUNCTIONAL REALLY VANCE TO THE

1976
01:15:12,683 --> 01:15:15,052
SOCIAL BEHAVIOR I TOLD YOU

1977
01:15:15,052 --> 01:15:17,120
ABOUT, MALE, FEMALE, SOCIAL

1978
01:15:17,120 --> 01:15:19,323
INTERACTIONS, BUT THEY'RE IN

1979
01:15:19,323 --> 01:15:20,924
THIS PUBLIC NETWORK AND WE NOW

1980
01:15:20,924 --> 01:15:22,059
FOR THE FIRST TIME HAVE ACCESS

1981
01:15:22,059 --> 01:15:23,327
TO ALL OF THEIR CONNECTIONS TO

1982
01:15:23,327 --> 01:15:28,765
START TO USE THIS INFORMATION TO

1983
01:15:28,765 --> 01:15:29,733
INFORM BEHAVIORIAL STUDIES.

1984
01:15:29,733 --> 01:15:31,234
OKAY, WE CAN KEEP GOING.

1985
01:15:31,234 --> 01:15:32,302
>> OKAY, SO, I DON'T KNOW
HOW

1986
01:15:32,302 --> 01:15:33,770
MUCH TIME I HAVE, LIKE I SAID

1987
01:15:33,770 --> 01:15:35,372
JUST CUT ME OFF WHEN YOU WANT

1988
01:15:35,372 --> 01:15:38,141
BUT I THOUGHT I WOULD GIVE YOU

1989
01:15:38,141 --> 01:15:40,344
AN EXAMPLE FROM OUR OWN WORK OF

1990
01:15:40,344 --> 01:15:42,379
HOW WE'RE ACTUALLY GOING TO GET

1991
01:15:42,379 --> 01:15:45,382
FROM CELL TYPES TO BEHAVIOR, AND

1992
01:15:45,382 --> 01:15:47,084
I THOUGHT THE EXAMPLE I WOULD

1993
01:15:47,084 --> 01:15:49,653
USE ARE THE VISUAL PROJECTION

1994
01:15:49,653 --> 01:15:50,354
RODS.

1995
01:15:50,354 --> 01:15:51,254
THERE'S A COUPLE INNOVATIONS

1996
01:15:51,254 --> 01:15:53,690
HERE SO THE VISUAL PROJECTION

1997
01:15:53,690 --> 01:15:55,258
ROTS ARE BETWEEN THE OPTIC LOBE

1998
01:15:55,258 --> 01:15:57,494
AND THE CENTRAL BRAIN AND THE

1999
01:15:57,494 --> 01:16:02,499
NUMEROUS CELL TYPE HERE ARE THE

2000
01:16:02,499 --> 01:16:04,568
NEURONS SEEN HERE, AND THEY'VE

2001
01:16:04,568 --> 01:16:06,837
BEEN STUDIED FOR MANY YEARS BY A

2002
01:16:06,837 --> 01:16:08,071
NUMBER OF DIFFERENT GROUPS BUT

2003
01:16:08,071 --> 01:16:10,006
NOW IN THE FLY CONNECTOME WE

2004
01:16:10,006 --> 01:16:11,508
KNOW WHAT THE COMPLETE CELL TYPE

2005
01:16:11,508 --> 01:16:13,477
IS AND IT'S PEAP, SO THERE'S

2006
01:16:13,477 --> 01:16:15,879
5000 OF THESE NEURONS THAT BE

2007
01:16:15,879 --> 01:16:16,813
BROKEN DOWN INTO 59 TYPES AND

2008
01:16:16,813 --> 01:16:21,051
EACH OF THESE TYPES TILES SOME

2009
01:16:21,051 --> 01:16:24,788
REGION OF THE VISUAL FIELD, OF

2010
01:16:24,788 --> 01:16:30,761
THE LOBULA, AND PROJECTS TO A

2011
01:16:30,761 --> 01:16:32,462
DISTINCT GLOMERULEUS AS CAN YOU

2012
01:16:32,462 --> 01:16:32,929
SEE HERE.

2013
01:16:32,929 --> 01:16:35,031
SO IF WE GO FORWARD, THIS

2014
01:16:35,031 --> 01:16:36,199
ARCHITECTURE IS NICE, IT ALLOWS

2015
01:16:36,199 --> 01:16:37,634
US TO ASK THE QUESTION, WHAT ARE

2016
01:16:37,634 --> 01:16:39,569
THE FUNCTIONS OF THESE CELL

2017
01:16:39,569 --> 01:16:40,837
TYPES AND SO, HOW DO YOU FIGURE,

2018
01:16:40,837 --> 01:16:43,073
IF YOU HAVE A CELL TYPE IN THE

2019
01:16:43,073 --> 01:16:44,508
CONNECTOME, HOW DO YOU FIGURE

2020
01:16:44,508 --> 01:16:45,442
OUT FUNCTION.

2021
01:16:45,442 --> 01:16:47,277
OKAY, SO THERE'S ACTUALLY MANY

2022
01:16:47,277 --> 01:16:49,045
NICE EXAMPLES NOW, I'M GOING TO

2023
01:16:49,045 --> 01:16:50,947
GIVE YOU 1 FROM SEBASTIAN'S

2024
01:16:50,947 --> 01:16:54,084
PAPER BUT I CAN POINT TO OTHERS

2025
01:16:54,084 --> 01:16:56,219
IF YOU'RE INTERESTED?

2026
01:16:56,219 --> 01:16:58,722
SO HERE SEBASTIAN LOOKS AT THE

2027
01:16:58,722 --> 01:17:00,257
DM3 CELLS IN THE OPERATING

2028
01:17:00,257 --> 01:17:02,092
GLOBALLYIC LOBE AND THEY COME IN

2029
01:17:02,092 --> 01:17:03,994
3 TYPES, P, Q, V, AND IF YOU

2030
01:17:03,994 --> 01:17:05,729
LOOK AT THEM, CAN YOU SEE THAT

2031
01:17:05,729 --> 01:17:08,698
THE DENDRITES ARE POINT INDEED 3

2032
01:17:08,698 --> 01:17:10,200
DIFFERENT DIRECTIONS, WHAT WE

2033
01:17:10,200 --> 01:17:13,103
CALL THE P-AXIS AND THE Q-AXIS

2034
01:17:13,103 --> 01:17:15,205
IS VERTICALAXIS AND SO IT LOADS

2035
01:17:15,205 --> 01:17:18,475
TO THE OBVIOUS HYPOTHESIS THAT

2036
01:17:18,475 --> 01:17:20,610
WHAT THESE DM3 CELLS DO IS THEY

2037
01:17:20,610 --> 01:17:23,113
DETECT 3 DIFFERENT BARS OF

2038
01:17:23,113 --> 01:17:24,648
DIFFERENT ORIENTATIONS, SEEM

2039
01:17:24,648 --> 01:17:25,649
VERY REASONABLE SO SEBASTIAN

2040
01:17:25,649 --> 01:17:27,384
COULD FOLLOW THE WIRES ALL THE

2041
01:17:27,384 --> 01:17:29,786
WAY DOWN TO THE LC NEURON CALLED

2042
01:17:29,786 --> 01:17:32,889
LC 15 AND HE COULD PREDICT THAT

2043
01:17:32,889 --> 01:17:34,758
LC 15 DETECTS BARS BUT OF ANY

2044
01:17:34,758 --> 01:17:36,092
ORIENTATION BECAUSE IT COMBINES

2045
01:17:36,092 --> 01:17:37,994
THEMSELVES AND IN FACT,

2046
01:17:37,994 --> 01:17:40,931
PHYSIOLOGY HAS BORN THAT OUT SO

2047
01:17:40,931 --> 01:17:42,833
THAT'S AN EXAMPLE OF JUST USING

2048
01:17:42,833 --> 01:17:45,502
CONNECTIVITY TO MAKE PREDICTIONS

2049
01:17:45,502 --> 01:17:46,236
ABOUT FUNCTION.

2050
01:17:46,236 --> 01:17:48,605
NOW THE OTHER OPTION IS

2051
01:17:48,605 --> 01:17:49,739
SIMULATION, TAKING THE CONNECT

2052
01:17:49,739 --> 01:17:51,708
OHM AND STARTING TO SIMULATE IT

2053
01:17:51,708 --> 01:17:52,809
TO   PERFORM FUNCTIONS AND THEN

2054
01:17:52,809 --> 01:17:55,445
SEEING WHAT THE NEURONS DO.

2055
01:17:55,445 --> 01:17:57,380
SO THERE'S BEEN A HANDFUL OF

2056
01:17:57,380 --> 01:17:59,316
EXAMPLES, THERE WAS A NICE PAPER

2057
01:17:59,316 --> 01:18:04,488
FROM KRISTIN SCOTT AND 1 FROM

2058
01:18:04,488 --> 01:18:05,355
TURAGA, I ENCOURAGE TO YOU LOOK

2059
01:18:05,355 --> 01:18:07,057
THEA THOSE EMPLOY I WILL TELL

2060
01:18:07,057 --> 01:18:09,025
YOU ABOUT OUR APPROACH WHICH IS

2061
01:18:09,025 --> 01:18:09,759
SLIGHTLY DIFFERENT.

2062
01:18:09,759 --> 01:18:10,727
WE CALL IT KNOCK OUT TRAINING

2063
01:18:10,727 --> 01:18:13,330
AND IT'S A WAY TO   PERFORM

2064
01:18:13,330 --> 01:18:15,966
SIMULATION TO ACTUALLY DISCOVER

2065
01:18:15,966 --> 01:18:17,234
THE FUNCTION OF CELL TYPES SO

2066
01:18:17,234 --> 01:18:20,704
MAWB I WILL JUST WALK YOU

2067
01:18:20,704 --> 01:18:22,105
THROUGH THAT IF YOU UNDULLING

2068
01:18:22,105 --> 01:18:22,272
ME.

2069
01:18:22,272 --> 01:18:26,243
SO WHAT WE WILL DO IS WE WILL

2070
01:18:26,243 --> 01:18:28,778
PREDICT THE MALES BEHAVIOR, SIS

2071
01:18:28,778 --> 01:18:30,180
SINGING AND CHASING USING ONLY

2072
01:18:30,180 --> 01:18:30,881
VISUAL INFORMATION FROM THE

2073
01:18:30,881 --> 01:18:34,117
FETAL COMPARTMENT  --

2074
01:18:34,117 --> 01:18:34,651
FEMALE.

2075
01:18:34,651 --> 01:18:35,919
SO THAT'S WHAT THE NETWORK WILL

2076
01:18:35,919 --> 01:18:36,620
DO.

2077
01:18:36,620 --> 01:18:36,920
KEEP GOING.

2078
01:18:36,920 --> 01:18:39,923
YES, OKAY, SO WHAT WE STARTED

2079
01:18:39,923 --> 01:18:42,726
WITH IS KNEE KNEW THERE WERE

2080
01:18:42,726 --> 01:18:44,494
GENETIC DRIVER LINES SHOWN HERE

2081
01:18:44,494 --> 01:18:45,996
THAT LABELED A LANDFUL OF THESE

2082
01:18:45,996 --> 01:18:48,565
LC TYPES AND SO YOU COULD SEE

2083
01:18:48,565 --> 01:18:50,867
EACH IMAGE HERE COMES FROM A

2084
01:18:50,867 --> 01:18:51,668
DIFFERENT GENETIC DRIVER LINE

2085
01:18:51,668 --> 01:18:52,836
LABELING DIFFERENT TYPE OF LC

2086
01:18:52,836 --> 01:18:57,207
AND THIS WAS PUBLISHED SOMETIME

2087
01:18:57,207 --> 01:19:02,612
AGO BY THE RISER CARD IN RUBEN

2088
01:19:02,612 --> 01:19:03,914
LABS, AND SO OVERTIME PEOPLE

2089
01:19:03,914 --> 01:19:05,815
USED THESE TO STUDY FUNCTIONS SO

2090
01:19:05,815 --> 01:19:07,284
THEY WOULD ACTIVATE THEM OR

2091
01:19:07,284 --> 01:19:08,218
SILENCE THEM.

2092
01:19:08,218 --> 01:19:10,487
HERE YOU SEE 1 EXAMPLE ASCRIBING

2093
01:19:10,487 --> 01:19:17,460
TO LC10, THE FUNCTION OF

2094
01:19:17,460 --> 01:19:18,929
ENCODING FEMALE ORIENTATION TO

2095
01:19:18,929 --> 01:19:22,198
DRIVE, AND ANOTHER EXAMPLE IS

2096
01:19:22,198 --> 01:19:24,701
LCLP2 THAT CODES THE SIZE OF A

2097
01:19:24,701 --> 01:19:26,937
LOOMING VISUAL OBLIGATIONS YECT

2098
01:19:26,937 --> 01:19:34,578
LIKE A PREDATOR ON THE RETINAL

2099
01:19:34,578 --> 01:19:35,545
 -- RETINA

2100
01:19:35,545 --> 01:19:36,279
AND LEADS TO TAKE OFF.

2101
01:19:36,279 --> 01:19:38,281
SO THIS TYPE OF CELL TYPE CODE

2102
01:19:38,281 --> 01:19:40,750
THAT EACH OF THESE LCs ENCODES

2103
01:19:40,750 --> 01:19:42,719
A PARTICULAR VISUAL FEATURE AND

2104
01:19:42,719 --> 01:19:43,787
DRIVES A PARTICULAR BEHAVIOR.

2105
01:19:43,787 --> 01:19:45,655
AND I THINK STUDYING THESE LCs

2106
01:19:45,655 --> 01:19:49,392
1 AT A TIME, ALL OF THIS DATA

2107
01:19:49,392 --> 01:19:51,127
WAS VERY CONSIST WENT THAT

2108
01:19:51,127 --> 01:19:52,529
HYPOTHESIS EMPLOY BUT WE

2109
01:19:52,529 --> 01:19:54,197
WONDERED IF THERE COULD BE A

2110
01:19:54,197 --> 01:19:55,599
MORE DISTRIBUTED CODE HERE, IF

2111
01:19:55,599 --> 01:19:56,633
THESE LCs GIVEN THEIR POSITION

2112
01:19:56,633 --> 01:19:58,268
IN THE VISUAL SYSTEM IS THE

2113
01:19:58,268 --> 01:20:00,337
BOTTLENECK BETWEEN THE EYE AND

2114
01:20:00,337 --> 01:20:01,271
THE BRAIN ACTUALLY ENCODED

2115
01:20:01,271 --> 01:20:02,706
INFORMATION AS A POPULATION.

2116
01:20:02,706 --> 01:20:06,109
SO IN THAT HYPOTHESIS, MOST OF

2117
01:20:06,109 --> 01:20:08,345
THESE WOULD PROCESS INFORMATION

2118
01:20:08,345 --> 01:20:10,046
ABOUT THE FEMALE AND WOULD DRIVE

2119
01:20:10,046 --> 01:20:10,313
BEHAVIOR.

2120
01:20:10,313 --> 01:20:13,883
OKAY, SO HOW DO WE TEST THAT?

2121
01:20:13,883 --> 01:20:14,951
SO, YOU KEEP GOING.

2122
01:20:14,951 --> 01:20:18,455
ALL RIGHT, SO WE BASICALLY

2123
01:20:18,455 --> 01:20:20,056
DESIGNED A DEEP NEURAL NETWORK,

2124
01:20:20,056 --> 01:20:21,591
THIS IS A FEED FORWARD NETWORK

2125
01:20:21,591 --> 01:20:23,960
AT THE MOMENT DOESN'T HAVE ANY

2126
01:20:23,960 --> 01:20:25,028
INFORMATION FROM THE CONNECT

2127
01:20:25,028 --> 01:20:28,264
O--METABOLIZED BUT IT HAS A

2128
01:20:28,264 --> 01:20:30,333
BOTTLENECK LAYER WHERE WE LABEL

2129
01:20:30,333 --> 01:20:31,601
IN THOSE TYPES AND THE YOB CORPS

2130
01:20:31,601 --> 01:20:33,670
IS TO TAKE THE VISUAL INPUT THE

2131
01:20:33,670 --> 01:20:36,006
MALE SEE ANDS IF YOU PREDICT HIS

2132
01:20:36,006 --> 01:20:39,909
-- OH, SORRY, YES AT THE

2133
01:20:39,909 --> 01:20:41,044
BOTTLENECK LAYER, THERE'S MAYBE

2134
01:20:41,044 --> 01:20:47,417
SOMETHING ELSE, OH YES, WE WILL

2135
01:20:47,417 --> 01:20:49,052
BASICALLY PREDICT THIS AT THE

2136
01:20:49,052 --> 01:20:50,887
BOTTLENECK BEHAVIOR, SO WE WILL

2137
01:20:50,887 --> 01:20:51,554
KEEP GOING.

2138
01:20:51,554 --> 01:20:55,125
SO THE INPUT IS MOVIES OF WHAT

2139
01:20:55,125 --> 01:20:57,394
THE MALE SEES AND FOR THIS WE

2140
01:20:57,394 --> 01:21:00,330
USED POST TRACKING METHOD MY LAB

2141
01:21:00,330 --> 01:21:01,998
DEVELOPED CALLED SLEEP.

2142
01:21:01,998 --> 01:21:05,368
IT WAS DEVELOPED BY CARERRA, AND

2143
01:21:05,368 --> 01:21:06,703
HE'S STILL IMPROVING IT AND

2144
01:21:06,703 --> 01:21:08,138
DEVELOPING THE METHODS BUILT ON

2145
01:21:08,138 --> 01:21:08,304
IT.

2146
01:21:08,304 --> 01:21:11,307
AND SO HERE THE POST TRACKING IS

2147
01:21:11,307 --> 01:21:12,742
CAPTURING FOR US THE FEMALE

2148
01:21:12,742 --> 01:21:14,444
NOTION RELATIVE TO THE MALE AND

2149
01:21:14,444 --> 01:21:18,648
YOU CAN SEE THE MOVIE ABOVE IS

2150
01:21:18,648 --> 01:21:22,719
SORT OF THE FEMALE FROM THE

2151
01:21:22,719 --> 01:21:23,620
MALE'S PERSPECTIVE.

2152
01:21:23,620 --> 01:21:25,088
APOLOGIES IF IT DOESN'T LOOK

2153
01:21:25,088 --> 01:21:26,923
LIKE THAT BUT THIS IS JUST A

2154
01:21:26,923 --> 01:21:27,190
RENDITION.

2155
01:21:27,190 --> 01:21:28,892
SO LET ME TELL YOU ABOUT THE

2156
01:21:28,892 --> 01:21:30,994
SILENCING, IN BLACK HERE IS A

2157
01:21:30,994 --> 01:21:31,828
CONTROL EXPERIMENT WHERE THE

2158
01:21:31,828 --> 01:21:33,596
MALE IS OF THE CONTROL GENOTYPE

2159
01:21:33,596 --> 01:21:35,298
AND YOU CAN SEE INITIALLY HE'S

2160
01:21:35,298 --> 01:21:37,133
FAR FROM THE FEMALE AND THEN

2161
01:21:37,133 --> 01:21:42,439
OVER TIME HE GETS CLOSER AND

2162
01:21:42,439 --> 01:21:44,908
THEN HERE'S TO EXAMPLES OF

2163
01:21:44,908 --> 01:21:46,109
SILENCING DIFFERENT LC NEURONS.

2164
01:21:46,109 --> 01:21:48,745
SO IF YOU SILENCE LC 10 A, CAN

2165
01:21:48,745 --> 01:21:49,846
YOU SEE THE MALE KIND OF NEVER

2166
01:21:49,846 --> 01:21:53,516
GETS CLOSE TO THE FEMALE, HE

2167
01:21:53,516 --> 01:21:57,587
STAYS FAR, AND IF YOU SILENCE

2168
01:21:57,587 --> 01:22:00,590
LC22, HE'S A SUPER COURTER, HE

2169
01:22:00,590 --> 01:22:01,891
PURSUES THE FEMALE RIGHT AWAY

2170
01:22:01,891 --> 01:22:03,727
AND STAYS CLOSE TO HER.

2171
01:22:03,727 --> 01:22:04,994
IF AND YOU LOOK ACROSS THE

2172
01:22:04,994 --> 01:22:06,362
SILENCING EXPERIMENTS AND WE DID

2173
01:22:06,362 --> 01:22:08,698
HUNDREDS OF THESE, CAN YOU SEE

2174
01:22:08,698 --> 01:22:10,900
THAT THE FEMALE CONITRATION IN

2175
01:22:10,900 --> 01:22:12,836
FRONT OF THE MALE VARIES ACROSS

2176
01:22:12,836 --> 01:22:14,137
THESE DIFFERENT SILENCING

2177
01:22:14,137 --> 01:22:18,341
EXPERIMENTS SO FOR LC10 A, SHE'S

2178
01:22:18,341 --> 01:22:19,776
SORT OF DIFFUSE, HE SPENTS VERY

2179
01:22:19,776 --> 01:22:22,912
LITTLE TIME WITH HER, AND FOR

2180
01:22:22,912 --> 01:22:24,147
LPLC, 2, HE REALLY FOCUSES ON

2181
01:22:24,147 --> 01:22:27,317
HER AND THEN IF YOU LOOK AT

2182
01:22:27,317 --> 01:22:30,754
SINGING WHAT YOU WILL SEE IS

2183
01:22:30,754 --> 01:22:32,155
THAT HE HAS THESE 2 TYPES OF

2184
01:22:32,155 --> 01:22:41,030
SONG HE CAN SING, 1 OF THEM IS A

2185
01:22:41,030 --> 01:22:43,500
SILENT SONG, SO LC10 A SPENDS

2186
01:22:43,500 --> 01:22:46,669
LESS TIME CLOSE TO THE FEMALE SO

2187
01:22:46,669 --> 01:22:50,106
THOSE GUYS SING LESS SONG, AND

2188
01:22:50,106 --> 01:22:50,840
LPLC2 MALES SPEND MORE TIME

2189
01:22:50,840 --> 01:22:55,111
CLOSE TO THE FEMALE AND THEY

2190
01:22:55,111 --> 01:22:56,346
SING MORE SONG, AND I WANTED TO

2191
01:22:56,346 --> 01:22:58,214
TELL YOU ABOUT THAT SLID THERE'S

2192
01:22:58,214 --> 01:22:59,449
A CONTINUUM OF PHENOTYPES.

2193
01:22:59,449 --> 01:23:02,919
I CAN JUST USE WORDS, THERE'S A

2194
01:23:02,919 --> 01:23:05,188
CONTINUUM OF PHENOTYPES, AND I

2195
01:23:05,188 --> 01:23:06,923
THINK THIS IS PRETTY COMMON FOR

2196
01:23:06,923 --> 01:23:08,792
YOU KNOW OPTICAL IMAGES O

2197
01:23:08,792 --> 01:23:10,860
GENETIC OR ANY KIND OF CAUSAL

2198
01:23:10,860 --> 01:23:11,861
MANIPULATION EXPERIMENT, YOU

2199
01:23:11,861 --> 01:23:13,329
WILL GET A GRADATION OF

2200
01:23:13,329 --> 01:23:14,364
PHENOTYPES AND YOU HAVE TO

2201
01:23:14,364 --> 01:23:16,332
FIGURE OUT WHAT TO DO WITH THIS

2202
01:23:16,332 --> 01:23:16,533
DATA.

2203
01:23:16,533 --> 01:23:18,434
OFTEN WE JUST FOCUS ON THE

2204
01:23:18,434 --> 01:23:19,669
BIGGEST PHENOTYPE AND MOVE

2205
01:23:19,669 --> 01:23:20,036
FORWARD.

2206
01:23:20,036 --> 01:23:20,870
BUT THERE'S SOMETHING QUITE

2207
01:23:20,870 --> 01:23:22,105
INTERESTING HERE THAT THERE'S

2208
01:23:22,105 --> 01:23:23,940
SUBTLE EFFECTS WITH A LOT OF

2209
01:23:23,940 --> 01:23:25,208
THESE LCs SO A WAY TO STUDY

2210
01:23:25,208 --> 01:23:27,510
THESE IN CONCERT IF YOU GO

2211
01:23:27,510 --> 01:23:28,845
FORWARD, NOW IS TO ACTUALLY

2212
01:23:28,845 --> 01:23:29,412
CREATE A MODEL.

2213
01:23:29,412 --> 01:23:32,415
SO IN THE MODEL, WE TAKE THAT

2214
01:23:32,415 --> 01:23:35,051
DEEP NEURAL NETWORK MODEL AND WE

2215
01:23:35,051 --> 01:23:37,554
SET 1 CHANNEL IN THE LC LAYER TO

2216
01:23:37,554 --> 01:23:39,489
0, WHEN WE'RE TRAINING THE

2217
01:23:39,489 --> 01:23:40,456
NETWORK ON BEHAVIORIAL DATA FROM

2218
01:23:40,456 --> 01:23:42,792
A MALE IN WHICH THAT LC WAS

2219
01:23:42,792 --> 01:23:43,059
SILENCED.

2220
01:23:43,059 --> 01:23:45,161
AND THEN IF YOU GO FORWARD, WE

2221
01:23:45,161 --> 01:23:47,096
WILL SET A DIFFERENT CHANNEL TO

2222
01:23:47,096 --> 01:23:49,299
0 WHEN WE TRAIN WITH DATA IN

2223
01:23:49,299 --> 01:23:50,767
WHICH A DIFFERENT LC TYPE WAS

2224
01:23:50,767 --> 01:23:52,368
SILENCED AND THEN IF YOU GO

2225
01:23:52,368 --> 01:23:53,903
FORWARD AGAIN, CAN YOU SEE --

2226
01:23:53,903 --> 01:23:55,305
SORRY THAT WAS IT BUT THE POINT

2227
01:23:55,305 --> 01:23:59,075
WAS THAT -- THAT'S FINE -- WE

2228
01:23:59,075 --> 01:24:01,444
BASICALLY TRAIN UP THE NETWORK

2229
01:24:01,444 --> 01:24:02,912
THAT THIS WAY, SO WE'RE

2230
01:24:02,912 --> 01:24:04,214
BASICALLY SILENCING 1 NOTE IN

2231
01:24:04,214 --> 01:24:05,882
THE DNN WHEN WE'RE TRAINING  ON

2232
01:24:05,882 --> 01:24:07,016
THIS BEHAVIORIAL DAT A. OKAY, SO

2233
01:24:07,016 --> 01:24:09,319
WE TRAIN UP THE WHOLE NETWORK,

2234
01:24:09,319 --> 01:24:11,621
WE HAVE A LAYER TRAINED SOLELY

2235
01:24:11,621 --> 01:24:12,856
ON BEHAVIORIAL DATA AND THEN WE

2236
01:24:12,856 --> 01:24:14,190
CAN ASK DOES THIS TRAIN NETWORK

2237
01:24:14,190 --> 01:24:17,293
DO A GOOD JOB OF PREDICTING HELD

2238
01:24:17,293 --> 01:24:18,528
OUT BEHAVIORIAL DATA AND IN FACT

2239
01:24:18,528 --> 01:24:19,062
IT DOES.

2240
01:24:19,062 --> 01:24:21,064
WE CALL IT TO 1 TO 1 NETWORK

2241
01:24:21,064 --> 01:24:23,766
BECAUSE THERE'S A 1 TO 1 MAPPING

2242
01:24:23,766 --> 01:24:26,269
OF LC NEURONS IN A MALE FLY AND

2243
01:24:26,269 --> 01:24:28,805
LC NEURONS IN THE NETWORK.

2244
01:24:28,805 --> 01:24:30,406
AND I JUST WANT TO TELL YOU

2245
01:24:30,406 --> 01:24:31,841
THERE THIS IS A BIT DIFFERENT

2246
01:24:31,841 --> 01:24:33,376
FROM OTHER NEURAL NETWORK MODELS

2247
01:24:33,376 --> 01:24:35,879
OF BEHAVIOR IN WHICH YOU MIGHT

2248
01:24:35,879 --> 01:24:37,947
SEE MAPPINGS BETWEEN NEURONS AND

2249
01:24:37,947 --> 01:24:39,649
LAYER OF THE NETWORK, HERE WE'RE

2250
01:24:39,649 --> 01:24:42,118
ACTUALLY MAPPING INDIVIDUAL

2251
01:24:42,118 --> 01:24:42,886
NOs OF NETWORK 2 INDIVIDUAL

2252
01:24:42,886 --> 01:24:43,887
NEURONS IN THE BRAIN.

2253
01:24:43,887 --> 01:24:46,956
SO IT DOES A GOOD JOB OF

2254
01:24:46,956 --> 01:24:47,924
PREDICTING BEHAVIOR, BUT WHAT'S

2255
01:24:47,924 --> 01:24:49,259
SURPRISING TO US, IF WE WENT TO

2256
01:24:49,259 --> 01:24:51,527
IMAGE IN THE MALES BRAIN, AND WE

2257
01:24:51,527 --> 01:24:54,130
RECORD FRIDAY LC NEURONS IN HIS

2258
01:24:54,130 --> 01:24:55,865
BRAIN, AND WE LOOKED AT THOSE

2259
01:24:55,865 --> 01:24:57,367
RESPONSES AND THEN WE COMPARED

2260
01:24:57,367 --> 01:25:00,503
THEM TO THE RESPONSES OF THE

2261
01:25:00,503 --> 01:25:01,938
NETWORK, SO THE NETWORK GOT THE

2262
01:25:01,938 --> 01:25:04,407
SAME MOVIE OF THE FEMALE AS THE

2263
01:25:04,407 --> 01:25:05,642
ACTUAL MALE DID AND WE ASKED

2264
01:25:05,642 --> 01:25:08,378
WHAT DOES THE ACTIVITY OF THE

2265
01:25:08,378 --> 01:25:09,646
NETWORK LOOK LIKE RELATIVE TO

2266
01:25:09,646 --> 01:25:10,079
HIS BRAIN.

2267
01:25:10,079 --> 01:25:11,281
AND IF YOU GO FORWARD, WHAT YOU

2268
01:25:11,281 --> 01:25:14,918
WILL SEE, IS WE FOUND THESE VERY

2269
01:25:14,918 --> 01:25:16,786
ASTOUNDING MATCHES, SO HERE JUST

2270
01:25:16,786 --> 01:25:18,955
EXAMPLES OF DIFFERENT LCs WE

2271
01:25:18,955 --> 01:25:20,924
RECORDED FROM AND THE NETWORK

2272
01:25:20,924 --> 01:25:21,224
RESPONSES.

2273
01:25:21,224 --> 01:25:23,493
SO THE MODEL NEVER SAW ANY

2274
01:25:23,493 --> 01:25:26,396
NEURAL DATA IT JUST WAS TRAINED

2275
01:25:26,396 --> 01:25:28,097
ON BEHAVIOR, BEHAVIORIAL

2276
01:25:28,097 --> 01:25:30,433
SILENCING DATA AND YET IT LEARNS

2277
01:25:30,433 --> 01:25:32,902
A NEURAL REPRESENTATION THAT

2278
01:25:32,902 --> 01:25:35,338
SEEMS TO MATCH THE ACTUAL DATA.

2279
01:25:35,338 --> 01:25:36,339
NOW THE   PERFOR THE PURPOSANCE

2280
01:25:36,339 --> 01:25:42,211
ACROSS  -- PERFORMANCE ACROSS AL

2281
01:25:42,211 --> 01:25:44,814
THE LCs IS MUCH BETTER THAN AN

2282
01:25:44,814 --> 01:25:45,848
UNTRAINED NETWORK BUT WE STILL

2283
01:25:45,848 --> 01:25:48,051
HAVE A WAYS TO GO AND I THINK

2284
01:25:48,051 --> 01:25:49,585
THAT WILL COME FROM PUTTING IN

2285
01:25:49,585 --> 01:25:52,188
THE REAL CONNECTIVITY FROM THE

2286
01:25:52,188 --> 01:25:52,488
CONNECTOME.

2287
01:25:52,488 --> 01:25:53,089
SO THIS IS ENCOURAGING FIELD

2288
01:25:53,089 --> 01:25:57,293
FUNCTIONS THINK WE CAN MAKE MORE

2289
01:25:57,293 --> 01:25:59,095
IMPROVEMENTS BY ADDING REAL

2290
01:25:59,095 --> 01:25:59,429
CONNECTIVITY.

2291
01:25:59,429 --> 01:26:00,463
ALL RIGHT, LET'S KEEP GOING.

2292
01:26:00,463 --> 01:26:02,398
SO OKAY, NOW WE GET TO INTERPRET

2293
01:26:02,398 --> 01:26:03,866
THE NETWORK AND THE FIRST THING

2294
01:26:03,866 --> 01:26:06,202
WE CAN ASK IS HOW DOES THIS LC

2295
01:26:06,202 --> 01:26:08,538
POPULATION REPRESENT THE FEMALE?

2296
01:26:08,538 --> 01:26:10,506
SO IF YOU GO FORWARD, YOU WILL

2297
01:26:10,506 --> 01:26:13,443
SEE, YEAH, THAT'S GOOD, WE CAN

2298
01:26:13,443 --> 01:26:15,111
MAKE MAPS OF VISUAL TUNING.

2299
01:26:15,111 --> 01:26:18,181
SO HERE WE BASICALLY PRESENTING

2300
01:26:18,181 --> 01:26:19,882
THE NETWORK WITH'S FEMALE OBJECT

2301
01:26:19,882 --> 01:26:22,685
THAT CAN CHANGE ORIENTATION, CAN

2302
01:26:22,685 --> 01:26:24,087
CHANGE SIZE OR IT CAN CHANGE

2303
01:26:24,087 --> 01:26:25,388
APPROXIMATION AND CAN YOU SEE

2304
01:26:25,388 --> 01:26:30,560
THE CELL TYPE LC10 A, PREFERS

2305
01:26:30,560 --> 01:26:33,363
FEMALES FACING AWAY THAT ARE

2306
01:26:33,363 --> 01:26:34,697
LARGE IN SIZE AND THAT ARE AT

2307
01:26:34,697 --> 01:26:35,965
THE CENTER OF THE VISUAL FIELD

2308
01:26:35,965 --> 01:26:37,633
AND THAT'S IN FACT WHAT WE KNOW

2309
01:26:37,633 --> 01:26:40,903
ABOUT THE CELL TYPE FROM A LOT

2310
01:26:40,903 --> 01:26:41,871
OF OTHER BEHAVIORIAL PHYSIOLOGY

2311
01:26:41,871 --> 01:26:42,205
WORK.

2312
01:26:42,205 --> 01:26:44,507
SO THAT IS GREAT.

2313
01:26:44,507 --> 01:26:45,742
OUR MODEL LEARNED REPRESENTATION

2314
01:26:45,742 --> 01:26:47,176
THAT MATCHES WHAT WE KNOW ABOUT

2315
01:26:47,176 --> 01:26:47,543
THE CELL TYPE.

2316
01:26:47,543 --> 01:26:49,212
BUT NOW IF YOU GO FORWARD, WE

2317
01:26:49,212 --> 01:26:50,880
ACTUALLY HAVE MAPS FOR EVERY

2318
01:26:50,880 --> 01:26:53,616
OTHER LC THAT WE MODELED AND

2319
01:26:53,616 --> 01:26:55,084
THERE'S A REAL BEAUTIFUL

2320
01:26:55,084 --> 01:26:57,920
DIVERSITY HERE, SO FOR EXAMPLE,

2321
01:26:57,920 --> 01:26:59,522
THE LPLC 2 NEURON I TOLD YOU

2322
01:26:59,522 --> 01:27:01,357
ABOUT THAT HAD ONLY BEEN -- CAN

2323
01:27:01,357 --> 01:27:05,161
YOU GO FORWARD -- HAD ONLY BEEN

2324
01:27:05,161 --> 01:27:06,362
IMPLICATED IN ESCAPE RESPONSES

2325
01:27:06,362 --> 01:27:08,264
WE SEE ACTUALLY IT DOES CARE

2326
01:27:08,264 --> 01:27:10,767
ABOUT FEMALE MOTION, BUT IN THE

2327
01:27:10,767 --> 01:27:11,734
PERIPHERY, THE VISUAL FIELD, SO

2328
01:27:11,734 --> 01:27:15,204
NOT IN THE CENTER BUT AT THE

2329
01:27:15,204 --> 01:27:15,405
EDGES.

2330
01:27:15,405 --> 01:27:18,341
THIS MIGHT EXPLAIN WHY TURNING

2331
01:27:18,341 --> 01:27:21,377
IT OFF MAKES MALES BETTER AT

2332
01:27:21,377 --> 01:27:23,679
PURSUING THE FEMALE, THIS LC31

2333
01:27:23,679 --> 01:27:25,248
ALSO PREFERS FEMALE IN THE

2334
01:27:25,248 --> 01:27:26,783
CENTER BUT ACROSS RANGES OF

2335
01:27:26,783 --> 01:27:28,451
SIZES AND I WILL IT YOU WHY

2336
01:27:28,451 --> 01:27:30,887
THAT'S IMPORTANT IN A MINUTE AND

2337
01:27:30,887 --> 01:27:32,955
THE LAST TYPE LC2, PREFERS

2338
01:27:32,955 --> 01:27:34,357
FEMALES IN THE CENTER BUT ONLY

2339
01:27:34,357 --> 01:27:36,459
WHEN THEY'RE FAR AWAY, SO THIS

2340
01:27:36,459 --> 01:27:38,694
GIVES AN IDEA OF HOW THEY MIGHT

2341
01:27:38,694 --> 01:27:41,264
WORK TOGETHER TO REPRESENT THE

2342
01:27:41,264 --> 01:27:42,465
RANGE OF DIFFERENCES THAT THE

2343
01:27:42,465 --> 01:27:44,333
MALE CARES ABOUT TO PRODUCE

2344
01:27:44,333 --> 01:27:44,867
THESE DIFFERENT BEHAVIORS.

2345
01:27:44,867 --> 01:27:46,636
AND I WANT TO POINT YOU TO

2346
01:27:46,636 --> 01:27:49,906
REALLY BEAUTIFUL PAPERS ALSO

2347
01:27:49,906 --> 01:27:52,575
STUDYING VISUAL TUNING IN THESE

2348
01:27:52,575 --> 01:27:53,342
LCs, LET'S GO FORWARD.

2349
01:27:53,342 --> 01:27:55,144
OKAY, SO NOW WE CAN DO A VERY

2350
01:27:55,144 --> 01:27:55,945
COOL EXPERIMENT WITH THE MODEL

2351
01:27:55,945 --> 01:27:59,282
THAT YOU JUST CANNOT DO WITH

2352
01:27:59,282 --> 01:28:01,017
DROSOPHILA GENETICS EVEN THOUGH,

2353
01:28:01,017 --> 01:28:04,754
YOU KNOW LI CHEN AND I WILL TELL

2354
01:28:04,754 --> 01:28:08,091
YOU CAN DO ALMOST ANYTHING WITH

2355
01:28:08,091 --> 01:28:09,459
DROSOPHILA GENETICS AND THAT IS

2356
01:28:09,459 --> 01:28:11,394
TO SILENCE OR ACTIVATE ANY

2357
01:28:11,394 --> 01:28:12,462
ARBITRARY NUMBER OF CELL TYPES.

2358
01:28:12,462 --> 01:28:13,796
ABOUT YOU HERE IN THE MODEL

2359
01:28:13,796 --> 01:28:15,431
BECAUSE WE MAPPED THESE NEURONS

2360
01:28:15,431 --> 01:28:17,266
INTO NODES IN THE MODEL, WE CAN

2361
01:28:17,266 --> 01:28:22,638
TURN OFF OR TURN ON ANY

2362
01:28:22,638 --> 01:28:23,906
ARBITRARY COMBINATION EMPLOY SO

2363
01:28:23,906 --> 01:28:25,274
HERE I'M SHOWING YOU HOW MANY

2364
01:28:25,274 --> 01:28:26,742
LCs WE WOULD HAVE TO SILENCE

2365
01:28:26,742 --> 01:28:30,113
TO GET NO CHANGE IN THE MALES

2366
01:28:30,113 --> 01:28:31,647
BEHAVIOR, IN THE MODEL AND HERE

2367
01:28:31,647 --> 01:28:33,116
I'M SHOWING YOU WHICH LCs WE

2368
01:28:33,116 --> 01:28:36,719
VALID TO TURN OFF TO COMPLETELY

2369
01:28:36,719 --> 01:28:37,753
BLOCK A PARTICULAR MALE

2370
01:28:37,753 --> 01:28:39,388
BEHAVIOR, SO THIS BASICALLY,

2371
01:28:39,388 --> 01:28:40,523
THESE KINDS OF EXPERIMENTS CAN

2372
01:28:40,523 --> 01:28:43,226
ESTABLISH FOR US THE SET OF LC

2373
01:28:43,226 --> 01:28:44,861
CELL TYPES THAT ARE NECESSARY

2374
01:28:44,861 --> 01:28:46,662
AND SUFFICIENT FOR BEHAVIOR.

2375
01:28:46,662 --> 01:28:48,231
SO IF YOU GO FORWARD, WHAT YOU

2376
01:28:48,231 --> 01:28:51,601
WILL SEE IS THAT THERE'S A VERY

2377
01:28:51,601 --> 01:28:52,535
RICH COMBINATORIAL CODE.

2378
01:28:52,535 --> 01:28:57,006
EACH LC TYPE, DRIVES A NUMBER OF

2379
01:28:57,006 --> 01:28:59,308
THE MALE COURTSHIP BEHAVIORS,

2380
01:28:59,308 --> 01:29:00,843
CHASING AND SINGING, AND EACH

2381
01:29:00,843 --> 01:29:05,648
BEHAVIOR IS DRIVEN BY MULTIPLE

2382
01:29:05,648 --> 01:29:06,382
LC TYPES.

2383
01:29:06,382 --> 01:29:07,850
SO THIS IS REALLY EXCITING

2384
01:29:07,850 --> 01:29:09,085
RESULT FOR US AND I WANT TO

2385
01:29:09,085 --> 01:29:10,353
UNPACK IT FOR YOU, SO THIS IS

2386
01:29:10,353 --> 01:29:11,888
GOING TO GET PRETTY CHALLENGING,

2387
01:29:11,888 --> 01:29:13,489
THERE'S A LOT OF ANIMATIONS ON

2388
01:29:13,489 --> 01:29:15,291
THE NEXT SLIDE THAT I'M SURE WE

2389
01:29:15,291 --> 01:29:16,626
WILL GET THROUGH IT.

2390
01:29:16,626 --> 01:29:16,926
KEEP GOING.

2391
01:29:16,926 --> 01:29:19,061
SO LET'S LOOK AT THIS CELL TYPE

2392
01:29:19,061 --> 01:29:21,330
LC31, SO GO FORWARD ONCE MORE.

2393
01:29:21,330 --> 01:29:25,568
WHAT WE SEE IS THAT LC31 IN THE

2394
01:29:25,568 --> 01:29:28,971
MODEL, DRIVES SONG AND IT DRIVES

2395
01:29:28,971 --> 01:29:33,876
WHEN STRONGLY ACTIVATED IT

2396
01:29:33,876 --> 01:29:35,711
DRIVES P-SLOW AND SIGN AND WHEN

2397
01:29:35,711 --> 01:29:37,380
WEEKLY ACTIVATED IT DRIVES FAST.

2398
01:29:37,380 --> 01:29:39,815
SO I DIDN'T TELL YOU ABOUT 2

2399
01:29:39,815 --> 01:29:41,984
TYPES OF SONG PULSES, 1 PRODUCED

2400
01:29:41,984 --> 01:29:44,487
FURTHER AWAY FROM FEMALES, 1

2401
01:29:44,487 --> 01:29:46,155
PRODUCED CLOSER.

2402
01:29:46,155 --> 01:29:47,023
LET'S GO FORWARD.

2403
01:29:47,023 --> 01:29:48,558
AND I TOLD THAT YOU MALES SING

2404
01:29:48,558 --> 01:29:50,560
DIFFERENT TYPES OF SONG, 1 NEAR

2405
01:29:50,560 --> 01:29:52,528
AND FAR, AND IN THE COMPLEX

2406
01:29:52,528 --> 01:29:54,063
SONG, HE SINGS SLOW PULSES AND

2407
01:29:54,063 --> 01:29:57,033
SIGN, AND THE SIMPLE SONG HE

2408
01:29:57,033 --> 01:29:57,833
SIGNS FAST PULSES.

2409
01:29:57,833 --> 01:29:59,969
ONCE MORE, AND SO WE RECENTLY

2410
01:29:59,969 --> 01:30:00,937
PUBLISHED A CIRCUIT MECHANISM

2411
01:30:00,937 --> 01:30:03,306
FOR THIS IN WHICH WE IMPLICATED

2412
01:30:03,306 --> 01:30:05,107
A CELL TYPE CALLED PC 2 AND

2413
01:30:05,107 --> 01:30:08,444
DRIVING THIS BEHAVIOR, WHEN PC 2

2414
01:30:08,444 --> 01:30:10,146
IS WEEKLY ACTIVATED YOU GET

2415
01:30:10,146 --> 01:30:12,682
AICISMEM SONG WITH FAST PULSES

2416
01:30:12,682 --> 01:30:15,284
AND WHEN PC 2 IS STRONGLY

2417
01:30:15,284 --> 01:30:19,555
ACTIVATED VIA THIS VERY NICE

2418
01:30:19,555 --> 01:30:22,091
DISINHIBITION MECHANISM, CAN YOU

2419
01:30:22,091 --> 01:30:23,392
DISINHIBIT THE SONG CIRCUITRY TO

2420
01:30:23,392 --> 01:30:25,194
DRIVE COMPLEX SINGING WHERE YOU

2421
01:30:25,194 --> 01:30:30,933
ALTERNATE BETWEEN THE SLOW

2422
01:30:30,933 --> 01:30:31,867
PULSES AND THE SIMPLE SONG.

2423
01:30:31,867 --> 01:30:33,302
SO WHAT WE SEE IN THE

2424
01:30:33,302 --> 01:30:35,471
CONNECTOME, I WILL BRING IT BACK

2425
01:30:35,471 --> 01:30:36,038
TO THE CONNECTOME.

2426
01:30:36,038 --> 01:30:40,509
IS THAT THE LC31 NEURON IS

2427
01:30:40,509 --> 01:30:41,510
DIRECTLY PRESYNAPTIC TO PC 2.

2428
01:30:41,510 --> 01:30:43,446
NOW THIS IS A FEMALE CONNECT

2429
01:30:43,446 --> 01:30:45,448
OHM, I'VE BEEN TELLING BUT MALE

2430
01:30:45,448 --> 01:30:47,016
BEHAVIOR, BUT IT'S VERY

2431
01:30:47,016 --> 01:30:48,751
INTRIGUING THAT THIS VISUAL CELL

2432
01:30:48,751 --> 01:30:50,620
TYPE THAT OUR MODEL FOUND TO

2433
01:30:50,620 --> 01:30:54,590
DRIVE SINGING IN THIS VERY

2434
01:30:54,590 --> 01:30:56,626
PARTICULAR WAY IS PRESYNAPTIC TO

2435
01:30:56,626 --> 01:30:58,894
A DIFFERENT SET OF STUDIES WE

2436
01:30:58,894 --> 01:31:00,029
FOUND IS IMPLICATE INDEED THE

2437
01:31:00,029 --> 01:31:01,864
CELL BEHAVIOR, SO THIS IS

2438
01:31:01,864 --> 01:31:05,635
ENCOURAGING RESULT FROM THE

2439
01:31:05,635 --> 01:31:06,836
CONNECTOME ABOUT THESE PIECES

2440
01:31:06,836 --> 01:31:07,470
BEING FIT TOGETHER.

2441
01:31:07,470 --> 01:31:09,238
SO NOW I THINK WE HAVE THE JOB

2442
01:31:09,238 --> 01:31:12,041
OF FIGURING OUT HOW ALL OF THESE

2443
01:31:12,041 --> 01:31:13,276
LC-TYPES ARE WORKING TOGETHER

2444
01:31:13,276 --> 01:31:14,577
VIA CONNECTIVITY TO DRIVE THISY

2445
01:31:14,577 --> 01:31:18,314
ABOUT HAD A FEW AND SO IF YOU GO

2446
01:31:18,314 --> 01:31:19,682
FORWARD AGAIN, I'LL JUST MENTION

2447
01:31:19,682 --> 01:31:22,451
TO YOU THAT LC31 HAPPENS TO BE 1

2448
01:31:22,451 --> 01:31:25,888
OF THE VISUAL PROJECTION NEURONS

2449
01:31:25,888 --> 01:31:28,357
HA IS FRUITLESS POSITIVE, THAT

2450
01:31:28,357 --> 01:31:29,558
IS IT EXPRESSES THAT

2451
01:31:29,558 --> 01:31:30,760
DISCIPLINARY MORPHIC FACTOR AND

2452
01:31:30,760 --> 01:31:32,795
HERE ARE 3 OTHER LC TYPES THAT

2453
01:31:32,795 --> 01:31:33,963
ARE ALSO FRUITLESS POSITIVE.

2454
01:31:33,963 --> 01:31:36,666
AND IF YOU GO FORWARD, WHAT WE

2455
01:31:36,666 --> 01:31:39,702
FIND IS THAT THESE NEURONS ARE

2456
01:31:39,702 --> 01:31:46,008
NOT PRIVATE, IN FACT, THEY ARE

2457
01:31:46,008 --> 01:31:49,078
IN A NETWORK IN WHICH THESE ARE

2458
01:31:49,078 --> 01:31:50,146
DEEPLY EMBEDDED TOGETHER AND IT

2459
01:31:50,146 --> 01:31:51,647
EXPLAINS TO US HOW IT IS THAT

2460
01:31:51,647 --> 01:31:55,284
THERE'S THE SHARED ROLES FOR

2461
01:31:55,284 --> 01:31:59,288
LCs IN DRIVING BEHAVIOR BUT

2462
01:31:59,288 --> 01:32:05,695
THESE NODES ARURE  NODES ARE DRD

2463
01:32:05,695 --> 01:32:11,467
THAT'S THE  THE HYPOTHESIS GOING

2464
01:32:11,467 --> 01:32:12,168
FORWARD.

2465
01:32:12,168 --> 01:32:14,403
GO FORWARD AGAIN, HERE YOU SEE

2466
01:32:14,403 --> 01:32:15,938
THE FEMALE CONNECTOME, BUT HERE

2467
01:32:15,938 --> 01:32:18,708
I PLOTTED THE LC'S WE STUDIED

2468
01:32:18,708 --> 01:32:20,876
WITH THE OPTICS IN THE LOBE, AND

2469
01:32:20,876 --> 01:32:23,079
ALL THE OPTIONS WE STUDIED DOWN

2470
01:32:23,079 --> 01:32:24,680
STREAM IN THE CENTRAL BRAIN, SO

2471
01:32:24,680 --> 01:32:27,683
THIS IS THE CONNECTIVITY WE'RE

2472
01:32:27,683 --> 01:32:29,251
CURRENTLY LAYERING INTO THE DEEP

2473
01:32:29,251 --> 01:32:32,021
NEURAL NELT WORK TO IMPROVE OUR

2474
01:32:32,021 --> 01:32:33,389
PREDICTIONS OF BEHAVIOR AND PUT

2475
01:32:33,389 --> 01:32:34,890
NOTHING THE REAL CONNECTIVITY

2476
01:32:34,890 --> 01:32:37,326
HELPS US TO GET CLOSER TO THE

2477
01:32:37,326 --> 01:32:38,627
REAL FLY BRAIN TO REALLY

2478
01:32:38,627 --> 01:32:40,930
UNDERSTAND HOW THIS KIND OF

2479
01:32:40,930 --> 01:32:43,332
POPULATION CODE AMONG THE LCs

2480
01:32:43,332 --> 01:32:46,001
CAN COORDINATE BE BEHAVIOR.

2481
01:32:46,001 --> 01:32:49,705
HOW IS IT THAT IT ENCODES

2482
01:32:49,705 --> 01:32:49,972
BEHAVIORS?

2483
01:32:49,972 --> 01:32:52,274
AND HOW IS IT THAT IT DRIVES

2484
01:32:52,274 --> 01:32:52,541
BEHAVIOR?

2485
01:32:52,541 --> 01:32:53,175
THAT'S WHAT WE'RE DOING NOW AND

2486
01:32:53,175 --> 01:32:56,245
I THINK THIS MAY BE MY LAST

2487
01:32:56,245 --> 01:32:56,746
SLIDE.

2488
01:32:56,746 --> 01:32:57,980
I THINK THE CHALLENGE FOR OUR

2489
01:32:57,980 --> 01:32:59,949
FIELD NOW THAT WE HAVE THESE

2490
01:32:59,949 --> 01:33:01,684
RICH DATA SET SYSTEM TO PREDICT

2491
01:33:01,684 --> 01:33:03,185
FUNCTION FROM THE CONNECT OHM SO

2492
01:33:03,185 --> 01:33:07,890
I TOLD BUT HOW WE'RE PREDICTING

2493
01:33:07,890 --> 01:33:08,824
NATURAL BEHAVIOR USING THIS

2494
01:33:08,824 --> 01:33:10,393
KNOCK OUT MECHANISM THEY THINK

2495
01:33:10,393 --> 01:33:13,062
MIGHT BE USEFUL TO A MEMBER OF

2496
01:33:13,062 --> 01:33:15,331
PEOPLE WE ALSO OBVIOUSLY COLLECT

2497
01:33:15,331 --> 01:33:16,599
VERY RICH NEURAL DATA, HERE'S AN

2498
01:33:16,599 --> 01:33:20,035
EXAMPLE OF A MALE FLY CHASING A

2499
01:33:20,035 --> 01:33:22,138
FEMALE, HE'S HEAD FIXED, RUNNING

2500
01:33:22,138 --> 01:33:24,473
ON A SPHREERICAL TREADMILL AND

2501
01:33:24,473 --> 01:33:25,207
WE'RE SIMULTANEOUSLY IMAGING HIS

2502
01:33:25,207 --> 01:33:26,942
BRAIN AND HERE WE EXPRESSED THE

2503
01:33:26,942 --> 01:33:27,977
CALCIUM SENSOR IN EVERY NEURON

2504
01:33:27,977 --> 01:33:29,512
AND WHAT I HOPE YOU CAN

2505
01:33:29,512 --> 01:33:32,548
APPRECIATE IS THAT THERE ARE

2506
01:33:32,548 --> 01:33:34,383
THESE BRAIN WIDE PATTERN THAT

2507
01:33:34,383 --> 01:33:35,518
ARE HAPPENING AS HE INTERACTING

2508
01:33:35,518 --> 01:33:35,985
WITH THE FEMALE.

2509
01:33:35,985 --> 01:33:37,386
THIS IS WHAT WE WANT TO EXPLAIN.

2510
01:33:37,386 --> 01:33:39,288
HOW DOES THIS RICH ACTIVITY

2511
01:33:39,288 --> 01:33:41,157
ARISE AND HOW IS IT BEING USED

2512
01:33:41,157 --> 01:33:44,160
TO DRIVE BEHAVIOR AND SO WE'RE

2513
01:33:44,160 --> 01:33:44,994
SORT OF GETTING THERE BY USING

2514
01:33:44,994 --> 01:33:47,430
THE CELL TYPES OF THE CONNECTOME

2515
01:33:47,430 --> 01:33:49,298
TO SORT OF GETITOUS THIS EVENT

2516
01:33:49,298 --> 01:33:52,601
GOAL AND I THINK WITH THAT --

2517
01:33:52,601 --> 01:33:54,069
OH, THE LAST THING I WANTED TO

2518
01:33:54,069 --> 01:33:56,839
SAY, I GUESS I HAVE 2 MORE

2519
01:33:56,839 --> 01:33:57,072
SLIDES.

2520
01:33:57,072 --> 01:34:02,978
I THINK OUR WORK NICELY FORMS A

2521
01:34:02,978 --> 01:34:05,881
FOUNDATION FOR THE CURRENT

2522
01:34:05,881 --> 01:34:09,452
FUNDING FURB WITHIN THE BRAIN

2523
01:34:09,452 --> 01:34:12,855
INITIATIVE CALLED BRAIN CONNECTS

2524
01:34:12,855 --> 01:34:15,124
AND TO GENERATE CONNECTOMES FOR

2525
01:34:15,124 --> 01:34:19,328
LARGER BRAINS LIKE MOUSE, AND

2526
01:34:19,328 --> 01:34:21,430
PRIMATE AND YOU KNOW OUR HOPE IS

2527
01:34:21,430 --> 01:34:23,098
THAT ALL THE GROUND WORK WE LAY

2528
01:34:23,098 --> 01:34:25,801
IN THE FLY, NOT JUST THE METHODS

2529
01:34:25,801 --> 01:34:30,439
BUT HOW WE DO CONNECTOMICS FOR

2530
01:34:30,439 --> 01:34:33,008
WORK AND OTHER PROJECTS

2531
01:34:33,008 --> 01:34:33,976
SEBASTIAN HAS TAKEN THOSE

2532
01:34:33,976 --> 01:34:35,177
INNOVATIONS AND APPLIED THEM

2533
01:34:35,177 --> 01:34:36,979
DIRECTLY BUT I ALSO SORT OF

2534
01:34:36,979 --> 01:34:38,047
MEANT THE FRAMEWORK, THE PATH

2535
01:34:38,047 --> 01:34:40,549
FOR FIGURING OUT FUNCTION FROM

2536
01:34:40,549 --> 01:34:43,052
THE CONNECTOME OUR HOPE IS THAT

2537
01:34:43,052 --> 01:34:44,987
THE WORK WE DO WILL DIRECTLY

2538
01:34:44,987 --> 01:34:46,255
INFORM THE STUDY BECAUSE IT'S

2539
01:34:46,255 --> 01:34:48,123
GOING TO GET MUCH MORE

2540
01:34:48,123 --> 01:34:49,091
COMPLICATED AS BRAINS GET BIGGER

2541
01:34:49,091 --> 01:34:51,794
AND I THINK MY LAST SLIDE IS A

2542
01:34:51,794 --> 01:34:57,566
THANK YOU TO MY LAB, ALSO THE

2543
01:34:57,566 --> 01:34:58,267
BRAIN INITTATIVE, SO EVERYTHING

2544
01:34:58,267 --> 01:35:00,603
I TOLD YOU ABOUT TODAY WAS

2545
01:35:00,603 --> 01:35:01,337
FUNDED THROUGH THE BRAIN

2546
01:35:01,337 --> 01:35:03,005
INITIATIVE AND IT'S AN EXAMPLE

2547
01:35:03,005 --> 01:35:06,075
OF RISK TAKING IN THESE PROJECTS

2548
01:35:06,075 --> 01:35:07,810
WERE ALL KIND OF CRAZY FROM THE

2549
01:35:07,810 --> 01:35:09,211
START BUT THAT I HAVE PAID OFF

2550
01:35:09,211 --> 01:35:10,246
IN INTERESTING WAYS SO THANK

2551
01:35:10,246 --> 01:35:10,613
YOU.

2552
01:35:10,613 --> 01:35:12,181
AND HAVE THIS PICTURE IN THE

2553
01:35:12,181 --> 01:35:13,182
LOWER RIGHT HAND CORNER, YAWN

2554
01:35:13,182 --> 01:35:15,618
AND HIS TEAM PAID US A VISIT AT

2555
01:35:15,618 --> 01:35:17,052
PRINCETON TO LOOK AT SOME OF THE

2556
01:35:17,052 --> 01:35:17,853
PROGRESS FROM THE BRAIN

2557
01:35:17,853 --> 01:35:20,089
INITIATIVE AND WE WERE JOINED BY

2558
01:35:20,089 --> 01:35:22,825
OUR LOCAL REPRESENTATIVE IN NEW

2559
01:35:22,825 --> 01:35:23,492
JERSEY BONNIE WATSON COLEMAN AND

2560
01:35:23,492 --> 01:35:27,663
IT WAS SORT OF A NICE WAY TO

2561
01:35:27,663 --> 01:35:29,064
SHOWCASE THE BRAIN INITIATIVE

2562
01:35:29,064 --> 01:35:33,235
WORK WHICH WAS, YEAH, REALLY

2563
01:35:33,235 --> 01:35:33,435
GREAT.

2564
01:35:33,435 --> 01:35:34,436
SO THANKS TO ALL OF YOU AND I'M

2565
01:35:34,436 --> 01:35:35,804
HAPPY TO TAKE QUESTIONS IF THERE

2566
01:35:35,804 --> 01:35:39,942
IS TIME AND THANK YOU SO MUCH LI

2567
01:35:39,942 --> 01:35:43,579
CHEN, FOR MAKING MY TALK

2568
01:35:43,579 --> 01:35:43,846
POSSIBLE.

2569
01:35:43,846 --> 01:35:44,580
>> THANKS MALA.

2570
01:35:44,580 --> 01:35:46,048
THAT WAS FABULOUS AS ALSO AND

2571
01:35:46,048 --> 01:35:51,186
THANKS LI CHEN FOR HELPING OUT.

2572
01:35:51,186 --> 01:35:51,687
REALLY APPRECIATE THAT.

2573
01:35:51,687 --> 01:35:53,255
IT'S GOOD TO BE AMONG FRIENDS

2574
01:35:53,255 --> 01:35:55,157
HERE SO LET'S TAKE QUESTIONS IF

2575
01:35:55,157 --> 01:35:55,691
FOLKS HAVE THEM.

2576
01:35:55,691 --> 01:36:03,332
I HAVE A BUNCH BUT I DON'T WANT

2577
01:36:03,332 --> 01:36:08,370
TO DOMINATE. 

2578
01:36:08,370 --> 01:36:13,342
>> LI, YOU'RE MUTED?

2579
01:36:13,342 --> 01:36:16,078
>> YOU MENTIONED ABOUT
PROTEIN

2580
01:36:16,078 --> 01:36:17,479
CONNECTOME INTO THE NETWORK.

2581
01:36:17,479 --> 01:36:20,482
FIRST OF ALL GREAT INTERPLAY

2582
01:36:20,482 --> 01:36:21,584
BETWEEN BIOLOGICAL AND NETWORK

2583
01:36:21,584 --> 01:36:28,390
AND ARTIFICIAL NETWORK SO YOU

2584
01:36:28,390 --> 01:36:29,959
MENTIONED INTO THE ARTIFICIAL

2585
01:36:29,959 --> 01:36:32,027
NETWORK TO HELP HIM PROVE, DO

2586
01:36:32,027 --> 01:36:35,097
YOU PUT SPECIFIC CONNECKS OR ARE

2587
01:36:35,097 --> 01:36:36,899
ALSO ADD WEIGHTS, SPECIFIC

2588
01:36:36,899 --> 01:36:37,132
WEIGHTS?

2589
01:36:37,132 --> 01:36:37,666
>> GREAT QUESTION.

2590
01:36:37,666 --> 01:36:39,935
SO THE WEIGHTS IN THE DROSOPHILA

2591
01:36:39,935 --> 01:36:41,937
CONNECTOME COME FROM THE NUMBER

2592
01:36:41,937 --> 01:36:42,438
OF CONNECTIONS.

2593
01:36:42,438 --> 01:36:44,607
I MENTIONED THAT WHEN 2 NEURONS

2594
01:36:44,607 --> 01:36:46,775
CONNECTS THEY OFTEN DO SO OVER

2595
01:36:46,775 --> 01:36:48,510
MULTIPLE TIN APSES AND SO WE USE

2596
01:36:48,510 --> 01:36:50,813
THE NUMBER OF SYNAPSES AS THE

2597
01:36:50,813 --> 01:36:51,046
WEIGHT.

2598
01:36:51,046 --> 01:36:54,316
AND THAT'S ACTUALLY WORKED OUT

2599
01:36:54,316 --> 01:36:58,787
WELL SO THERE'S A VERY NICE

2600
01:36:58,787 --> 01:37:00,956
PAPER FROM A COLLEAGUE WHO USED

2601
01:37:00,956 --> 01:37:03,192
THE CONNECTION OF WEIGHTS AND

2602
01:37:03,192 --> 01:37:03,759
GOT GOOD RESULTS.

2603
01:37:03,759 --> 01:37:05,794
SO I THINK AS PEOPLE ARE

2604
01:37:05,794 --> 01:37:07,162
SIMULATING THE CONNECTOME, THIS

2605
01:37:07,162 --> 01:37:08,330
PROXY FOR WEIGHT SEEMS TO WORK

2606
01:37:08,330 --> 01:37:10,065
WELL, BUT IT'S NOT GOING TO WORK

2607
01:37:10,065 --> 01:37:11,433
IN ALL CASES AND I THINK IT WILL

2608
01:37:11,433 --> 01:37:12,534
BE INTERESTING TO FIGURE OUT

2609
01:37:12,534 --> 01:37:14,003
WHERE IT FAILS AND WHAT OTHER

2610
01:37:14,003 --> 01:37:21,810
INFORMATION WE HAVE TO ADD.

2611
01:37:21,810 --> 01:37:22,978
SO WE USUALLY DON'T TRIM THE

2612
01:37:22,978 --> 01:37:25,047
WEIGHT, WE USE THE NUMBER OF

2613
01:37:25,047 --> 01:37:26,515
SYNAPSES FROM THE CONNECTOME AS

2614
01:37:26,515 --> 01:37:28,584
THE WEIGHTS AND IT'S A VERY

2615
01:37:28,584 --> 01:37:31,086
USEFUL, I WOULD SAY COON STRAINT

2616
01:37:31,086 --> 01:37:33,022
IN THE MODELS BECAUSE OTHERWISE

2617
01:37:33,022 --> 01:37:37,993
MODEL WOULD BE WAY TO

2618
01:37:37,993 --> 01:37:40,529
UNCONSTRAINED. 

2619
01:37:40,529 --> 01:37:42,665
>> THANKS.

2620
01:37:42,665 --> 01:37:43,565
NTED?

2621
01:37:43,565 --> 01:37:45,634
>> HELLO, HI, MALA, THANKS
FOR A

2622
01:37:45,634 --> 01:37:46,468
TERRIFIC TALK, GLAD IT CAME

2623
01:37:46,468 --> 01:37:46,902
THROUGH.

2624
01:37:46,902 --> 01:37:48,504
HAD A QUESTION, MORE A

2625
01:37:48,504 --> 01:37:50,072
SPECULATIVE QUESTION, BUT AS YOU

2626
01:37:50,072 --> 01:37:51,306
YOU KNOW REALLY LOOK UNDER THE

2627
01:37:51,306 --> 01:37:53,742
HOOD OF THIS AND DONE TROUBLE

2628
01:37:53,742 --> 01:37:56,412
SHOOTING AND THOUGHT ABOUT THESE

2629
01:37:56,412 --> 01:37:57,279
CIRCUITS IS THERE ANYTHING YOU

2630
01:37:57,279 --> 01:37:59,348
CAN LEARN FROM HOW THESE MIGHT

2631
01:37:59,348 --> 01:38:03,585
DEVELOP FROM WHAT YOU SEE IN

2632
01:38:03,585 --> 01:38:04,653
YOUR DATA.

2633
01:38:04,653 --> 01:38:06,422
>> YEAH, NO, THAT'S A
GREAT

2634
01:38:06,422 --> 01:38:07,189
QUESTION.

2635
01:38:07,189 --> 01:38:09,091
I THINK ACTUALLY HAVING

2636
01:38:09,091 --> 01:38:11,760
CONNECTOMES, LEADS NATURALLY TO

2637
01:38:11,760 --> 01:38:12,861
QUESTIONS ABOUT DEVELOPMENTAL

2638
01:38:12,861 --> 01:38:13,996
BIOLOGY AND ABOUT CELL BIOLOGY.

2639
01:38:13,996 --> 01:38:16,732
SO I THINK THERE'S THIS NICE

2640
01:38:16,732 --> 01:38:18,701
FEEDBACK LOOP THAT I'M VERY

2641
01:38:18,701 --> 01:38:19,134
EXCITED ABOUT.

2642
01:38:19,134 --> 01:38:21,370
ONE OF THE PROJECTS WE'RE

2643
01:38:21,370 --> 01:38:24,606
PLANNING TO INITIATE HERE IS 1

2644
01:38:24,606 --> 01:38:28,510
OF BUILDING PEOPLE DEVELOPMENT

2645
01:38:28,510 --> 01:38:30,813
CONNECTOME SO PICKING DIFFERENT

2646
01:38:30,813 --> 01:38:31,980
TIME POINTS ALONG PEOPLE

2647
01:38:31,980 --> 01:38:33,716
DEVELOPMENT AND THAT'S SORT OF

2648
01:38:33,716 --> 01:38:34,817
INTERESTING BECAUSE THE NERVOUS

2649
01:38:34,817 --> 01:38:37,786
SYSTEM IS RESTRUCTURING DURING

2650
01:38:37,786 --> 01:38:38,387
METAMORPHOSIS DURING THE FLY.

2651
01:38:38,387 --> 01:38:39,722
>> ESPECIALLY IN THE FLY,
IT'S

2652
01:38:39,722 --> 01:38:40,823
CRAZY TO SEE.

2653
01:38:40,823 --> 01:38:42,591
>> YEAH, THERE IS A NICE

2654
01:38:42,591 --> 01:38:45,527
DEVELOPMENTAL TIME SERIES FOR

2655
01:38:45,527 --> 01:38:50,766
THE C-ELEGANS CONNECTED BY MASON

2656
01:38:50,766 --> 01:38:52,868
AND I YEAH, I DON'T HAVE MUCH

2657
01:38:52,868 --> 01:38:54,336
MORE TO SAY ABOUT THAT BECAUSE

2658
01:38:54,336 --> 01:38:57,539
THERE IS SO MUCH I CAN INFER

2659
01:38:57,539 --> 01:38:59,174
ABOUT DEVELOPMENT AFTER LOOKING

2660
01:38:59,174 --> 01:39:00,976
AT THE ADULT BRAIN CONNECTOME,

2661
01:39:00,976 --> 01:39:03,345
BUT HAVING THE CONNECTOME LEADS

2662
01:39:03,345 --> 01:39:06,615
NATURALLY  TO QUESTIONS ABOUT

2663
01:39:06,615 --> 01:39:08,350
DEVELOPMENT AND I'M HOPING WE

2664
01:39:08,350 --> 01:39:10,652
CAN ANSWER SOME OF THOSE.

2665
01:39:10,652 --> 01:39:13,789
>> AND YEAH, THERE ARE
STUDIES

2666
01:39:13,789 --> 01:39:15,357
WHERE THEY PEOPLE HAVE TRAINED

2667
01:39:15,357 --> 01:39:16,692
LARVAY AND SEEING HOW THAT MIGHT

2668
01:39:16,692 --> 01:39:19,027
WORK OUT IN CONNECT CAN ALSO

2669
01:39:19,027 --> 01:39:20,462
EVENTUALLY BE INTERESTING.

2670
01:39:20,462 --> 01:39:22,064
THANKS A LOT.

2671
01:39:22,064 --> 01:39:26,335
>> YEAH, SURE.

2672
01:39:26,335 --> 01:39:26,602
>> JIM?

2673
01:39:26,602 --> 01:39:28,070
>> >>  THANKS MALA,
REALLY

2674
01:39:28,070 --> 01:39:28,370
FASCINATING.

2675
01:39:28,370 --> 01:39:33,709
I WAS WONDERING IF YOU COULD

2676
01:39:33,709 --> 01:39:38,781
INPUT ANYTHING ABOUTASTROCYTE

2677
01:39:38,781 --> 01:39:39,848
INTERACTIONS AND DIFFERENCES IN

2678
01:39:39,848 --> 01:39:41,884
THE BEHAVIOR AND THOSE

2679
01:39:41,884 --> 01:39:42,217
INTERACTIONS.

2680
01:39:42,217 --> 01:39:42,885
>> YEAH, THAT'S A GREAT

2681
01:39:42,885 --> 01:39:43,152
QUESTION.

2682
01:39:43,152 --> 01:39:45,053
SO I DIDN'T TALK ABOUT TBLIA AT

2683
01:39:45,053 --> 01:39:48,924
ALL BUT THERE IS GLUE MARIOUSA

2684
01:39:48,924 --> 01:39:49,424
 GLUE MARIOUS --

2685
01:39:49,424 --> 01:39:52,094
GLUE MARIOUSA IN THE DATA SET,

2686
01:39:52,094 --> 01:39:53,295
AND THERE HAVE BEEN -- SO THE

2687
01:39:53,295 --> 01:39:55,898
PART OF THE FLY WORK CONSORTIUM

2688
01:39:55,898 --> 01:39:57,199
IN OUR DISSEMINATION EFFORT HAS

2689
01:39:57,199 --> 01:40:00,469
BEEN TO GET NEW SCIENTISTS

2690
01:40:00,469 --> 01:40:01,303
INVOLVED IN DATA.

2691
01:40:01,303 --> 01:40:02,771
COMPUTER SCIENTISTS WHO WANT TO

2692
01:40:02,771 --> 01:40:04,973
DO MODELING ON THESE KINDS OF

2693
01:40:04,973 --> 01:40:06,008
COMPLEX GRAPHS BUT ALSO

2694
01:40:06,008 --> 01:40:09,711
SCIENTISTS WHO WORK ON

2695
01:40:09,711 --> 01:40:10,612
NONNEURONAL CELLS AND YEAH, SO

2696
01:40:10,612 --> 01:40:14,116
THERE'S A FEW PEOPLE WORKING ON

2697
01:40:14,116 --> 01:40:15,651
GLIA, I THINK THERE'S MORE WORK

2698
01:40:15,651 --> 01:40:17,786
TO DO SO FIRST WE HAVE TO CLEAN

2699
01:40:17,786 --> 01:40:21,957
UP THE GLIA AND THEN LOOK AT

2700
01:40:21,957 --> 01:40:22,825
PROXIMITY TO DIFFERENT CELL

2701
01:40:22,825 --> 01:40:23,025
TYPES.

2702
01:40:23,025 --> 01:40:24,893
WE DON'T HAVE A LOT OF

2703
01:40:24,893 --> 01:40:25,494
INFORMATION IN THE CONNECTOME

2704
01:40:25,494 --> 01:40:27,629
YET BUT THERE ARE A GROUPS

2705
01:40:27,629 --> 01:40:29,064
WORKING ON ADDING THAT

2706
01:40:29,064 --> 01:40:30,465
INFORMATION TRYING TO REGISTER

2707
01:40:30,465 --> 01:40:32,134
IT INTO IMAGES WHERE THEY STAY

2708
01:40:32,134 --> 01:40:33,402
FOR THESE DIFFERENT RECEPTORS SO

2709
01:40:33,402 --> 01:40:34,369
I THINK THERE'S EXCITING THINGS

2710
01:40:34,369 --> 01:40:36,572
THAT CAN COME OUT OF THIS, BUT

2711
01:40:36,572 --> 01:40:37,873
IT'S YOU KNOW, I ENCOURAGE

2712
01:40:37,873 --> 01:40:39,107
ANYBODY WHO WANTS TO WORK ON

2713
01:40:39,107 --> 01:40:41,343
THESE QUESTIONS TO GET INVOLVED.

2714
01:40:41,343 --> 01:40:43,111
YEAH. 

2715
01:40:43,111 --> 01:40:44,580
NTHANK YOU.

2716
01:40:44,580 --> 01:40:47,850
NTHANKS MALA, I WAS GOING TO ASK

2717
01:40:47,850 --> 01:40:50,652
YOU WHAT DO WE KNOW ABOUT

2718
01:40:50,652 --> 01:40:52,421
MOLECULAR CORRELATES WITH THESE

2719
01:40:52,421 --> 01:40:53,822
ANATOMICALLY IDENTIFIED CELL

2720
01:40:53,822 --> 01:40:54,022
TYPES.

2721
01:40:54,022 --> 01:40:56,058
AND 1 REASON WE BRING IT UP IS

2722
01:40:56,058 --> 01:40:59,361
WHEN WE INITIALLY LOOKED AT THE

2723
01:40:59,361 --> 01:41:00,529
TLFR PROJECTIONS IN THE MOUSE

2724
01:41:00,529 --> 01:41:03,298
CORTEX THERE WAS NOT A 1 TO 1

2725
01:41:03,298 --> 01:41:06,702
CORRESPONDENCE BETWEEN WHAT WE

2726
01:41:06,702 --> 01:41:09,171
DEFINE TRANSCRIPTOMICLY AS A

2727
01:41:09,171 --> 01:41:10,239
CLUSTER, VERSUS THE TARGET.

2728
01:41:10,239 --> 01:41:11,607
AND THAT COULD BE BECAUSE YOU

2729
01:41:11,607 --> 01:41:13,275
KNOW THERE'S OTHER THINGS GOING

2730
01:41:13,275 --> 01:41:15,744
ON OR IT COULD BE SOMETHING THAT

2731
01:41:15,744 --> 01:41:17,579
HAPPENED THAT'S NO LONGER

2732
01:41:17,579 --> 01:41:17,813
PRESENT.

2733
01:41:17,813 --> 01:41:19,281
SO HOW FAR ARE WE GETTING WITH

2734
01:41:19,281 --> 01:41:21,516
THAT TO IDENTIFY IF YOU CLASSIFY

2735
01:41:21,516 --> 01:41:25,921
THE CELL AS X, VERSUS Y, WHAT

2736
01:41:25,921 --> 01:41:28,924
DID YOU FIND THE UNIQUE

2737
01:41:28,924 --> 01:41:31,260
MOLECULAR IDENTITIES WITH THOSE

2738
01:41:31,260 --> 01:41:31,627
CLASSIFICATIONS.

2739
01:41:31,627 --> 01:41:34,830
>> SO THERE'S BEEN QUITE A
LOT

2740
01:41:34,830 --> 01:41:36,999
OF TRANSCRIPTIONAL WORK DONE IN

2741
01:41:36,999 --> 01:41:38,033
DROSOPHILA DOFFILLA, AND WE CAN

2742
01:41:38,033 --> 01:41:41,370
ACTUALLY AND I WOULD SAY,

2743
01:41:41,370 --> 01:41:43,438
PROBABLY THE BEST COMPARISON IN

2744
01:41:43,438 --> 01:41:45,807
THE OPTIC LOBE WHERE WE HAD

2745
01:41:45,807 --> 01:41:47,976
2/3RD CELL TYPES REALLY WELL

2746
01:41:47,976 --> 01:41:49,945
DEFINED BUT SO FAR AS I

2747
01:41:49,945 --> 01:41:50,946
UNDERSTAND THE NUMBER OF

2748
01:41:50,946 --> 01:41:52,047
TRANSCRIPTIONAL TYPE SYSTEM

2749
01:41:52,047 --> 01:41:52,614
LOWER.

2750
01:41:52,614 --> 01:41:54,683
AND THAT COULD BE FOR 1 OF 2

2751
01:41:54,683 --> 01:41:57,019
REASONS, 1 IS THAT WE HAVEN'T

2752
01:41:57,019 --> 01:41:59,621
SEQUENCED DEEPLY ENOUGH OR

2753
01:41:59,621 --> 01:42:00,989
THERE'S DEGENERACY WHERE YOU GET

2754
01:42:00,989 --> 01:42:03,392
MOWLT PEL TYPES FROM 1

2755
01:42:03,392 --> 01:42:04,426
TRANSCRIPTIONAL TYPE AND I THINK

2756
01:42:04,426 --> 01:42:05,661
THOSE IS ITS ARE ONGOING AND WE

2757
01:42:05,661 --> 01:42:06,995
WILL HAVE TO SEE HOW IT TURNS

2758
01:42:06,995 --> 01:42:08,397
OUT BUT I DO WANT TO ENCOURAGE

2759
01:42:08,397 --> 01:42:10,499
PEOPLE TO THINK ABOUT CONNECT

2760
01:42:10,499 --> 01:42:12,000
OMIC TYPES.

2761
01:42:12,000 --> 01:42:13,969
TO ME IT'S KIND OF THE GOLD

2762
01:42:13,969 --> 01:42:14,670
STANDARD.

2763
01:42:14,670 --> 01:42:15,771
HAVE YOU THE COMPLETE

2764
01:42:15,771 --> 01:42:19,341
CONNECTIVITY SO YOU CAN KNOW

2765
01:42:19,341 --> 01:42:21,176
EXACTLY WHETHER 2 CELLS ARE OF

2766
01:42:21,176 --> 01:42:23,779
THE SAME TYPE OR DIFFERENT.

2767
01:42:23,779 --> 01:42:26,982
AND SO FAR IT SEEMS TO BE OF

2768
01:42:26,982 --> 01:42:28,317
HIGHER RESOLUTION THAN JUST

2769
01:42:28,317 --> 01:42:29,017
TRANSCRIPTIONAL PROFILES, AT

2770
01:42:29,017 --> 01:42:31,787
LEAST IN THE FLY:  SO I THINK

2771
01:42:31,787 --> 01:42:32,955
THAT'S SOMETHING THAT WILL COME

2772
01:42:32,955 --> 01:42:35,057
OUT OF THESE NEW DATA SETS LIKE

2773
01:42:35,057 --> 01:42:37,459
MICRONS IS SORT OF WHAT ARE THE

2774
01:42:37,459 --> 01:42:38,727
CELL TYPES, I'M EXCITED TO SEE

2775
01:42:38,727 --> 01:42:40,095
THE RESULTS OF THAT.

2776
01:42:40,095 --> 01:42:41,229
>> YEAH AND AS MENTIONED
BEFORE,

2777
01:42:41,229 --> 01:42:42,698
WE DON'T KNOW THE DEVELOPMENTAL

2778
01:42:42,698 --> 01:42:45,067
HISTORY, IT COULD BE THAT YOU

2779
01:42:45,067 --> 01:42:48,236
KNOW THINGS WERE EXPRESSED TO

2780
01:42:48,236 --> 01:42:50,305
SPECIFY INTRUCTIVELY EARLY THAT

2781
01:42:50,305 --> 01:42:53,308
ARE NOW GONE O IT COULD BE THAT

2782
01:42:53,308 --> 01:42:55,177
THESE TRANSCRIPTIONAL STATE OR

2783
01:42:55,177 --> 01:42:56,812
PHENOTYPES OR PATTERNS OR SIMPLY

2784
01:42:56,812 --> 01:42:58,213
  PERMISSIVE FOR MULTIPLE

2785
01:42:58,213 --> 01:43:00,282
DIFFERENT TYPES OF CONNECTIVITY

2786
01:43:00,282 --> 01:43:02,417
PATTERNS BUT IT WOULD BE REALLY

2787
01:43:02,417 --> 01:43:06,688
COOL TO SORT THAT OUT.

2788
01:43:06,688 --> 01:43:07,389
ROY?

2789
01:43:07,389 --> 01:43:08,857
>> MALA, THAT WAS
INCREDIBLE

2790
01:43:08,857 --> 01:43:10,459
EMPLOY I LOVE TED'S QUESTION

2791
01:43:10,459 --> 01:43:11,259
ABOUT DEVELOPMENT.

2792
01:43:11,259 --> 01:43:12,661
I WILL GO THE DIRECTION OF

2793
01:43:12,661 --> 01:43:12,961
DISEASE.

2794
01:43:12,961 --> 01:43:16,765
I WOULD LOVE TO KNOW WHAT YOUR

2795
01:43:16,765 --> 01:43:17,599
THOUGHTS ARE WHEN AND HOW WE

2796
01:43:17,599 --> 01:43:20,969
MIGHT BE ABLE TO START USING

2797
01:43:20,969 --> 01:43:21,636
THESE BEAUTIFUL CONNECTOMIC MAPS

2798
01:43:21,636 --> 01:43:23,472
TO START TO GIVE US INSIGHT,

2799
01:43:23,472 --> 01:43:24,539
PREDICTIONS ABOUT DISEASE AND

2800
01:43:24,539 --> 01:43:27,309
MAYBE EVEN JUST DYSFUNCTION AND

2801
01:43:27,309 --> 01:43:29,544
SYMPTOMS RELATED TO DISEASE,

2802
01:43:29,544 --> 01:43:31,113
MAYBE NOT THE ACTUAL DISEASE?

2803
01:43:31,113 --> 01:43:32,714
>> YEAH, THANK YOU ROY FOR
THE

2804
01:43:32,714 --> 01:43:35,350
QUESTION AND WELCOME TO THE

2805
01:43:35,350 --> 01:43:35,550
MCWG.

2806
01:43:35,550 --> 01:43:35,951
>> THANK YOU.

2807
01:43:35,951 --> 01:43:38,553
>> AND I THINK THIS IS
GREAT AND

2808
01:43:38,553 --> 01:43:39,821
I ACTUALLY THINK DROSOPHILA CAN

2809
01:43:39,821 --> 01:43:41,890
BE OF SIGNIFICANT VALUE BECAUSE

2810
01:43:41,890 --> 01:43:43,458
WE KNOW THAT MANY DISORDERS ARE

2811
01:43:43,458 --> 01:43:44,960
CAUSE BY DEFECTS AND BRAIN

2812
01:43:44,960 --> 01:43:46,294
WIRING BUT WE DON'T KNOW HOW.

2813
01:43:46,294 --> 01:43:48,063
WHAT IS THE CONQUENCE FOR

2814
01:43:48,063 --> 01:43:49,698
FUNCTION THAT COMES FROM THAT

2815
01:43:49,698 --> 01:43:52,134
DEFECT AND WIRING, AND SO, YOU

2816
01:43:52,134 --> 01:43:55,303
KNOW THERE'S I THINK SORT OF 2

2817
01:43:55,303 --> 01:43:55,937
POSSIBLE AVENUES IN DROSOPHILA,

2818
01:43:55,937 --> 01:43:58,373
1 IS WE HAVE VERY LARGE

2819
01:43:58,373 --> 01:43:59,441
COLLECTIONS OF MUTANTS FOR

2820
01:43:59,441 --> 01:44:00,642
PARTICULAR GENES, ALMOST EVERY

2821
01:44:00,642 --> 01:44:02,577
HUMAN DISEASE GENE HAS A

2822
01:44:02,577 --> 01:44:03,945
HOMOLOGUE IN DROSOPHILA, AND SO

2823
01:44:03,945 --> 01:44:05,614
WE REQUEST START TO USE THESE

2824
01:44:05,614 --> 01:44:07,282
IMAGINE HAVE YOU A COMPLOAT

2825
01:44:07,282 --> 01:44:09,684
CONNECT OHM OF A MUTANT, NOW YOU

2826
01:44:09,684 --> 01:44:10,752
CAN -- NOW CAN YOU FIGURE OUT

2827
01:44:10,752 --> 01:44:13,221
WHERE ALL THE CHANGES AND WIRING

2828
01:44:13,221 --> 01:44:15,257
ARE AND HOW THEY MIGHT RELATE TO

2829
01:44:15,257 --> 01:44:15,524
FUNCTION.

2830
01:44:15,524 --> 01:44:16,558
THE OTHER THING I THINK WE CAN

2831
01:44:16,558 --> 01:44:18,727
DO IS ACTUALLY START TO MODEL

2832
01:44:18,727 --> 01:44:21,897
THE CONNECTOME SO WE CAN CREATE

2833
01:44:21,897 --> 01:44:23,198
ARTIFICIAL MISWIRING.

2834
01:44:23,198 --> 01:44:24,533
WHICH CONNECKS CAN WE GET RID OF

2835
01:44:24,533 --> 01:44:27,169
SORT OF LIKE I TOLD YOU WITH THE

2836
01:44:27,169 --> 01:44:27,502
MODELING.

2837
01:44:27,502 --> 01:44:29,538
HOW MANY CAN WE REMOVE AND HAVE

2838
01:44:29,538 --> 01:44:31,473
NO EFFECT ON THE BEHAVIOR?

2839
01:44:31,473 --> 01:44:32,841
HOW MANY CAN WE ADDED SO WHERE

2840
01:44:32,841 --> 01:44:35,077
THE PLACES IN THE NETWORK THAT

2841
01:44:35,077 --> 01:44:36,845
ARE SENSITIVE TO PUSH TURBATION,

2842
01:44:36,845 --> 01:44:39,514
AND I KNOW THE DROSOPHILA BRAIN

2843
01:44:39,514 --> 01:44:41,583
IS NOT ORGANIZED LIKE THE

2844
01:44:41,583 --> 01:44:42,851
MAMMALIAN BRAIN BUT IT SHARES A

2845
01:44:42,851 --> 01:44:45,554
LOT OF SAME COMPUTATIONS SO

2846
01:44:45,554 --> 01:44:46,021
THERE'S FUNCTIONS THAT

2847
01:44:46,021 --> 01:44:46,822
DROSOPHILA   PERFOR THE PURPOSES

2848
01:44:46,822 --> 01:44:49,991
LIKE NAVIGATION, THAT WE KNOW

2849
01:44:49,991 --> 01:44:51,560
ARE COMPLETELY CONSERVED WITH

2850
01:44:51,560 --> 01:44:53,061
MAMMALIAN BRAINS, NOT ONLY AT

2851
01:44:53,061 --> 01:44:54,262
THE COMPUTATIONAL LEVEL BUT ALSO

2852
01:44:54,262 --> 01:44:55,597
IN IMPLEMENTATION, SO I THINK

2853
01:44:55,597 --> 01:44:56,531
THAT'S ENCOURAGING TO THINK

2854
01:44:56,531 --> 01:45:00,435
ABOUT USING THE BEHAVIORIAL READ

2855
01:45:00,435 --> 01:45:02,871
OUT AS A WAY TO PROBE THESE

2856
01:45:02,871 --> 01:45:04,106
QUESTIONS AND I THINK IT'S

2857
01:45:04,106 --> 01:45:05,874
EBSIGHTING AND I'M HOPING WE CAN

2858
01:45:05,874 --> 01:45:11,480
USE FLIES TO MAKE MORE PROGRESS

2859
01:45:11,480 --> 01:45:14,850
THERE. 

2860
01:45:14,850 --> 01:45:15,550
>> TERRIFIC, OTHER
QUESTIONS?

2861
01:45:15,550 --> 01:45:17,819
CAN I ASK ANOTHER 1.

2862
01:45:17,819 --> 01:45:19,054
SO YOU DESCRIBE BEAUTIFULLY THE

2863
01:45:19,054 --> 01:45:22,190
LC NEURONS IN THE MALES, THE

2864
01:45:22,190 --> 01:45:24,326
ACTIVITY OF COURSE USING THE

2865
01:45:24,326 --> 01:45:26,428
FEMALE MAP, BUT YOU HAVE THE

2866
01:45:26,428 --> 01:45:29,397
HEMIBRAIN TO COMPARE IT TO AND

2867
01:45:29,397 --> 01:45:32,300
THE HEMIBRAIN WAS FROM A MALE,

2868
01:45:32,300 --> 01:45:32,567
MALE FLY.

2869
01:45:32,567 --> 01:45:34,569
>> IT WAS A FEMALE.

2870
01:45:34,569 --> 01:45:35,737
>> OH WAS IT?

2871
01:45:35,737 --> 01:45:39,307
>> YEAH, SO IT WAS A
DIFFERENT

2872
01:45:39,307 --> 01:45:39,441
--

2873
01:45:39,441 --> 01:45:41,243
>> NEVER MIND.

2874
01:45:41,243 --> 01:45:41,977
NEVER MIND.

2875
01:45:41,977 --> 01:45:44,412
>> ACTUALLY I DO THINK, SO
WE'VE

2876
01:45:44,412 --> 01:45:46,781
ACTUALLY JUST IMAGED A MALE

2877
01:45:46,781 --> 01:45:50,018
BRAIN AT PRINCETON AND GENELIA

2878
01:45:50,018 --> 01:45:51,920
ALSO HAS A MALE BRAIN THAT

2879
01:45:51,920 --> 01:45:52,988
THEY'RE PLANNING TO RELEASE, AND

2880
01:45:52,988 --> 01:45:57,192
SO I THINK WE WILL HAVE THESE

2881
01:45:57,192 --> 01:45:58,994
SEX DIFFERENT DATA SETS.

2882
01:45:58,994 --> 01:46:01,997
BUT IN THE NERVE CORD, THE

2883
01:46:01,997 --> 01:46:05,133
FEMALE BRAIN AND NERVE CORD, THE

2884
01:46:05,133 --> 01:46:09,838
BRAIN IS A FEMALE, BUT G, NELIA

2885
01:46:09,838 --> 01:46:10,639
ALREADY RELEASED A MALE NERVE

2886
01:46:10,639 --> 01:46:12,707
CORD AND THERE WE CAN MAKE

2887
01:46:12,707 --> 01:46:13,775
DETAILED COMPARISONS BETWEEN THE

2888
01:46:13,775 --> 01:46:14,442
MALE AND FEMALE.

2889
01:46:14,442 --> 01:46:15,911
IT'S QUITE INTERESTING BECAUSE

2890
01:46:15,911 --> 01:46:17,712
IT TURNS OUT IT'S NOT TRIVIAL TO

2891
01:46:17,712 --> 01:46:19,314
FIGURE OUT WHICH NEURONS ARE

2892
01:46:19,314 --> 01:46:20,782
JUST DIFFERENT BECAUSE THESE ARE

2893
01:46:20,782 --> 01:46:22,317
2 DIFFERENT FLIES, AND WHICH

2894
01:46:22,317 --> 01:46:23,485
NEURONS ARE DIFFERENT BECAUSE

2895
01:46:23,485 --> 01:46:24,719
THEY'RE FROM 2 DIFFERENT SEXES

2896
01:46:24,719 --> 01:46:26,588
AND THAT'S KIND OF AN

2897
01:46:26,588 --> 01:46:27,923
INTERESTING COMPUTATIONAL

2898
01:46:27,923 --> 01:46:29,424
PROBLEM WE'VE BEEN WRESTLING

2899
01:46:29,424 --> 01:46:32,727
WITH RECENTLY.

2900
01:46:32,727 --> 01:46:33,228
>> GREAT, THANKS, TOR?

2901
01:46:33,228 --> 01:46:33,962
>> YEAH, THANKS, I THOUGHT
THAT

2902
01:46:33,962 --> 01:46:35,730
WAS REALLY BEAUTIFUL AND THE

2903
01:46:35,730 --> 01:46:36,932
NEURAL NETWORK IS AMAZING THAT

2904
01:46:36,932 --> 01:46:40,402
YOU COULD TRAIN IT WITHOUT ANY

2905
01:46:40,402 --> 01:46:41,503
DIRECT TRAINING ON NEURAL

2906
01:46:41,503 --> 01:46:43,238
ACTIVITY THAT YOU'RE STARTING TO

2907
01:46:43,238 --> 01:46:44,072
PREDICT THE ACTIVITY OF THESE

2908
01:46:44,072 --> 01:46:46,608
DIFFERENT TYPES OF CELLS.

2909
01:46:46,608 --> 01:46:49,878
IF I UNDERSTAND RIGHT AND

2910
01:46:49,878 --> 01:46:50,145
BEHAVIOR.

2911
01:46:50,145 --> 01:46:53,415
I'M WONDERING IF YOU COULD

2912
01:46:53,415 --> 01:46:54,683
SPECULATE ON THE LIMITS OF

2913
01:46:54,683 --> 01:46:55,784
PREDICTABILITY, SO YOU WILL

2914
01:46:55,784 --> 01:46:57,953
PREDICT A LOT BUT YOU KNOW WHAT

2915
01:46:57,953 --> 01:47:02,324
DO YOU THINK IS IRREDUCIBLE OR

2916
01:47:02,324 --> 01:47:07,796
RELEASED OR STOCHASTIC AND WHAT

2917
01:47:07,796 --> 01:47:08,697
DO YOU PREDICT?

2918
01:47:08,697 --> 01:47:10,432
>> YEAH, I GUESS I SORT OF

2919
01:47:10,432 --> 01:47:11,366
SUGGESTED 1 DIRECTION WHICH IS

2920
01:47:11,366 --> 01:47:13,101
THAT WE MIGHT NEED TO PUT IN

2921
01:47:13,101 --> 01:47:14,569
MORE INFORMATION ABOUT THE WIRES

2922
01:47:14,569 --> 01:47:15,837
TO IMPROVE OUR PREDICTABILITY

2923
01:47:15,837 --> 01:47:17,038
BUT I THINK WHAT YOU'RE GETTINGA

2924
01:47:17,038 --> 01:47:20,976
THE IS THAT BEHAVIORS KIND OF

2925
01:47:20,976 --> 01:47:21,910
VARIABLE AND YOU KNOW WHAT'S

2926
01:47:21,910 --> 01:47:25,647
SORT OF THE LIMIT OF

2927
01:47:25,647 --> 01:47:28,049
PREDICTABILITY WITH THESE

2928
01:47:28,049 --> 01:47:28,283
MODELS.

2929
01:47:28,283 --> 01:47:29,517
I DON'T HAVE A GREAT ANSWER TO

2930
01:47:29,517 --> 01:47:30,752
THE QUESTION BECAUSE IT IS TRUE

2931
01:47:30,752 --> 01:47:32,454
THAT WE'RE, YOU KNOW WE'RE

2932
01:47:32,454 --> 01:47:33,955
TRAINING THE MODELS WITH THIS

2933
01:47:33,955 --> 01:47:35,457
LARGE DATA SET.

2934
01:47:35,457 --> 01:47:36,858
WE PREDICT THE DAILY BASIS THEA,

2935
01:47:36,858 --> 01:47:39,194
IT DOES A GOOD JOB BUT HOW WELL

2936
01:47:39,194 --> 01:47:41,162
DOES IT CAPTURE VARIATION, THIS

2937
01:47:41,162 --> 01:47:42,497
MODEL IS DETERMINISTIC IN A WAY

2938
01:47:42,497 --> 01:47:45,400
AND IT DOESN'T HAVE SORT OF

2939
01:47:45,400 --> 01:47:46,468
STOCHASTIC NOISE AND

2940
01:47:46,468 --> 01:47:46,835
VARIABILITY.

2941
01:47:46,835 --> 01:47:49,304
SO I THINK THAT'S AN INTERESTING

2942
01:47:49,304 --> 01:47:51,172
QUESTION AND I THINK CAN THINK

2943
01:47:51,172 --> 01:47:53,575
OF WAYS TO EXPLORE IT, SO I CAN

2944
01:47:53,575 --> 01:47:54,876
THINK OF EXPERIMENTS WHAT COULD

2945
01:47:54,876 --> 01:47:58,513
DO WITH MODELING TO GET AT THIS

2946
01:47:58,513 --> 01:48:00,115
ISSUE BUT 1 THING I WOULD SAY IS

2947
01:48:00,115 --> 01:48:01,616
THAT AS THESE NEW DATA SETS COME

2948
01:48:01,616 --> 01:48:02,617
ON LINE AND AS WEB CONNECTED

2949
01:48:02,617 --> 01:48:05,120
ACTUALLY START TO YOU KNOW

2950
01:48:05,120 --> 01:48:06,354
FIGURE OUT HOW INDIVIDUALS

2951
01:48:06,354 --> 01:48:07,589
DIFFER AND WHERE THEY DIFFER IN

2952
01:48:07,589 --> 01:48:09,924
THE CIRCUITS, I THINK WE WILL

2953
01:48:09,924 --> 01:48:10,525
HAVE MUCH BETTER HYPOTHESIS,

2954
01:48:10,525 --> 01:48:12,827
ABOUT SORT OF THE ORIGINS OF

2955
01:48:12,827 --> 01:48:14,296
INDIVIDUAL VARIABILITY AND WHAT

2956
01:48:14,296 --> 01:48:15,864
IT MEANS FOR ROBUSTNESS.

2957
01:48:15,864 --> 01:48:18,199
YOU KNOW SOME OF THESE

2958
01:48:18,199 --> 01:48:18,733
BEHAVIORS, THESE COURTSHIP

2959
01:48:18,733 --> 01:48:20,702
BEHAVIORS THEY'RE SO CRITICAL TO

2960
01:48:20,702 --> 01:48:22,637
THE LIFE OF AN ANIMAL.

2961
01:48:22,637 --> 01:48:24,005
YOU DON'T WANT TO MESS THEM UP.

2962
01:48:24,005 --> 01:48:25,940
YOU WILL NOT BE SUCCESSFUL AS A

2963
01:48:25,940 --> 01:48:29,744
SUITOR OR AS A MATE AND SO WHERE

2964
01:48:29,744 --> 01:48:30,545
DOES THAT ROBUSTNESS COME FROM

2965
01:48:30,545 --> 01:48:32,147
IF I THINK WE WILL HAVE BETTER

2966
01:48:32,147 --> 01:48:36,318
ANSWERS TO THAT SOON.

2967
01:48:36,318 --> 01:48:44,392
>> THANKS.

2968
01:48:44,392 --> 01:48:46,628
OH JOHN, YOU'RE MUTED.

2969
01:48:46,628 --> 01:48:47,295
SORRY.

2970
01:48:47,295 --> 01:48:47,829
>> SORRY.

2971
01:48:47,829 --> 01:48:49,164
NO IT'S REALLY COOL, I'M REALLY

2972
01:48:49,164 --> 01:48:51,299
EXCITED TO SEE KLF-TWO OTHER

2973
01:48:51,299 --> 01:48:53,368
CONNECTOMES AND I IMAGINE IF 1

2974
01:48:53,368 --> 01:48:55,537
IS INTERESTED IN A SPECIFIC

2975
01:48:55,537 --> 01:48:57,205
BEHAVIOR, 1 COULD HONE IN ON

2976
01:48:57,205 --> 01:49:00,909
THAT SUBREGION AND DO MORE

2977
01:49:00,909 --> 01:49:01,142
SAMPLES.

2978
01:49:01,142 --> 01:49:05,780
SO, HOW MUCH FASTER -- OKAY, THE

2979
01:49:05,780 --> 01:49:07,716
FIRST 1'S ALWAYS GOING TO BE THE

2980
01:49:07,716 --> 01:49:10,552
HARDEST TO DO BUT IN TERMS OF

2981
01:49:10,552 --> 01:49:11,286
THE SEGMENTATION ALGORITHMS,

2982
01:49:11,286 --> 01:49:13,822
WHERE ARE YOU IN TERMS OF PROOF

2983
01:49:13,822 --> 01:49:14,489
READING?

2984
01:49:14,489 --> 01:49:17,792
I MEAN IN TERMS OF SPEED AND

2985
01:49:17,792 --> 01:49:18,093
EFFICIENCY?

2986
01:49:18,093 --> 01:49:20,595
HOW MUCH FASTER IS IT?

2987
01:49:20,595 --> 01:49:24,466
>> YEAH, SO, YOU KNOW THE

2988
01:49:24,466 --> 01:49:26,701
HEMIBRAIN THAT WAS 22,000

2989
01:49:26,701 --> 01:49:28,770
NEURONS TOOK 50 PERSON YEARS TO

2990
01:49:28,770 --> 01:49:29,637
PROOF READ, THAT 50 PEOPLE

2991
01:49:29,637 --> 01:49:33,007
WORKING FULL IF I'M FOR A YEAR

2992
01:49:33,007 --> 01:49:35,944
-- FULL-TIME FOR A YEAR, OUR

2993
01:49:35,944 --> 01:49:38,146
PROJECT WHICH IS 140,000 NEURONS

2994
01:49:38,146 --> 01:49:39,514
TOOK LESS THAN 3 YEARS.

2995
01:49:39,514 --> 01:49:42,951
BUT THE DATA IS DEFINITE BUT THE

2996
01:49:42,951 --> 01:49:44,419
PROCESS HAS GOTTEN FASTER AND

2997
01:49:44,419 --> 01:49:45,220
THERE'S ALSO LOTS OF OTHER

2998
01:49:45,220 --> 01:49:47,222
PIECES OF THE PIPELINE THAT HAVE

2999
01:49:47,222 --> 01:49:49,424
IMPROVED THAT SPEED AT WHICH WE

3000
01:49:49,424 --> 01:49:52,260
CAN IMAGE, YOU KNOW THE SPEED AT

3001
01:49:52,260 --> 01:49:53,661
WHICH WE CAN RECONSTRUCT THESE

3002
01:49:53,661 --> 01:49:55,563
VOLUMES, SO THIS IS WHAT I MEANT

3003
01:49:55,563 --> 01:49:58,366
IS THAT IT'S SO VALUABLE TO DO

3004
01:49:58,366 --> 01:50:01,736
THESE SMALLER CONNECT OHMS

3005
01:50:01,736 --> 01:50:03,304
BECAUSE AND THIS DIDN'T GET

3006
01:50:03,304 --> 01:50:06,307
WASTED AND EVERY PIPELINE

3007
01:50:06,307 --> 01:50:08,343
INCORPORATES THE PRIOR ADVANCES

3008
01:50:08,343 --> 01:50:10,612
BUT YEAH, I THINK WE'RE AT A

3009
01:50:10,612 --> 01:50:13,148
STAGE NOW WHERE YOU KNOW IT'S

3010
01:50:13,148 --> 01:50:17,419
NOT A FANTASY TO THINK ABOUT ROY

3011
01:50:17,419 --> 01:50:21,089
ASKED THIS QUESTION, CAN YOU DO

3012
01:50:21,089 --> 01:50:22,223
CONNECTOMES AND MUTANTS AND

3013
01:50:22,223 --> 01:50:25,026
THINK ABOUT DISEASE, I DON'T

3014
01:50:25,026 --> 01:50:26,161
THINK UNREASONABLE TO HAVE THAT

3015
01:50:26,161 --> 01:50:27,228
BE PART OF THE PROJECT.

3016
01:50:27,228 --> 01:50:29,030
I WILL TAKE A MUTANT AND A

3017
01:50:29,030 --> 01:50:30,331
CONNECTOME AND LOOK AT THE

3018
01:50:30,331 --> 01:50:30,698
RESULTS.

3019
01:50:30,698 --> 01:50:31,900
SO I THINK WE'RE GETTING INTO

3020
01:50:31,900 --> 01:50:34,169
THE A REGION WHERE WE'RE AT

3021
01:50:34,169 --> 01:50:34,936
LEAST FOR DROSOPHILA, IT'S

3022
01:50:34,936 --> 01:50:36,104
POSSIBLE, AND 1 OF THE REASONS

3023
01:50:36,104 --> 01:50:38,072
IS THAT THERE'S SO MANY

3024
01:50:38,072 --> 01:50:40,875
AUTOMATED WAYS NOW, ONCE YOU

3025
01:50:40,875 --> 01:50:43,044
HAVE 1 CONNECTOME TO USE THAT

3026
01:50:43,044 --> 01:50:43,778
INFORMATION TO AUTOMATICALLY

3027
01:50:43,778 --> 01:50:52,253
PROOF READ THE NEXT 1. 

3028
01:50:52,253 --> 01:50:53,988
>> RIGHT, RIGHT, RIGHT.

3029
01:50:53,988 --> 01:50:55,123
TERRIFIC, WE'RE ACTUALLY RUNNING

3030
01:50:55,123 --> 01:50:57,992
A FEW MINUTES AHEAD DESPITE OF

3031
01:50:57,992 --> 01:51:02,363
THE GLYPHS, THANKS MALA, AND

3032
01:51:02,363 --> 01:51:04,232
THANK YOU LIQUN, I HAVE TO SEND

3033
01:51:04,232 --> 01:51:05,767
HIM A BOTTLE OF WINE OR

3034
01:51:05,767 --> 01:51:08,069
SOMETHING, I WILL BE SENDING HIM

3035
01:51:08,069 --> 01:51:10,338
A PRESENT, TOO.

3036
01:51:10,338 --> 01:51:11,506
OKAY, SUSAN WHAT SHOULD WE --

3037
01:51:11,506 --> 01:51:12,974
WHAT DO YOU WANT TO DO ABOUT THE

3038
01:51:12,974 --> 01:51:14,976
BREAK IN.

3039
01:51:14,976 --> 01:51:16,778
>> WELL, WE ARE SCHEDULED
TO

3040
01:51:16,778 --> 01:51:17,345
BREAK UNTIL 2:30.

3041
01:51:17,345 --> 01:51:18,980
I THINK WE SHOULD LEAVE IT AT

3042
01:51:18,980 --> 01:51:19,347
THAT.

3043
01:51:19,347 --> 01:51:21,416
WE GET 4 EXTRA MINUTES.

3044
01:51:21,416 --> 01:51:23,251
JUST A REMIND THEY'RE YOU WILL

3045
01:51:23,251 --> 01:51:26,120
HAVE TO RESIGN IN INTO THIS NEW

3046
01:51:26,120 --> 01:51:26,888
MICROSOFT TEAMS LINK.

3047
01:51:26,888 --> 01:51:30,024
SO, WE WILL SEE YOU IN ABOUT A

3048
01:51:30,024 --> 01:51:31,526
HALF AN HOUR.

3049
01:51:31,526 --> 01:51:35,530
>> THANKS EVERYBODY.

3050
01:51:35,530 --> 01:51:37,732
>> APPRECIATE IT.

3051
01:51:37,732 --> 01:51:39,300
>> BYE?

3052
01:51:39,300 --> 01:51:49,477
THANK YOU.