GOOD AFTERNOON, EVERYONE.
WELCOME TO THE WALS WEDNESDAY
AFTERNOON LECTURE SERIES TODAY.
I'M GRACE WONG, PROGRAM DIRECTOR
NATIONAL INSTITUTE OF
NEUROLOGICAL DISORDERS AND
STROKE AND I ALSO WORK FOR THE
BRAIN INITIATIVE. I'M DELIGHT TO
BED HERE TODAY. BUT BEFORE WE
START SOME VERY IMPORTANT
INFORMATION FOR THOSE OF YOU WHO
ARE HERE FOR CONTINUING MEDICAL
EDUCATION CREDITS THE CODE IS,
LOOK AT THAT, 57659. IF YOU ARE
ATTENDING VIRTUALLY, AND HAVE
QUESTIONS, PLEASE HIT THE SEND
LIVE FEEDBACK ON THE VIDEOCAST
PAGE AND YOUR QUESTIONS WILL BE
ASKED AT THE END OF THE LECTURE.
SO IT IS MY PLEASURE TO
INTRODUCE OUR SPEAKER TODAY, DR.
BRAD AIMONE, WHO I HAVE ACTUALLY
KNOWN FOR UP TO A DECADE. WE
JUST DISCOVERED THIS OVER LUNCH.
I REALLY GOT TO KNOW BRAD IN
2020 AT MY PREVIOUS JOB AS A
PROGRAM DIRECTOR AT THE NATIONAL
SCIENCE FOUNDATION. I STARTED A
PROGRAM EMERGING FRONTIERS AN
RESEARCH INNOVATION PROGRAM ON
BRAIN IRSPIRED DYNAMICS FOR
INTERRELATED CIRCUIT AND
ARTIFICIAL INTELLIGENCE AND BRAD
WAS GENEROUS WITH WITH HIS
PERSPECTIVE AND KNOWLEDGE. HE IS
A DISTINGUISHED FELLOW AT SANDIA
NATIONAL LAB, HE LED SEVERAL
RESEARCH PROJECTS THAT LEVERAGES
COMPUTATIONAL NEUROSCIENCE TO
ADVANCE ARTIFICIAL INTELLIGENCE.
AND IN USING NEUROMORPHIC
COMPUTING PLATFORMS FOR FUTURE
ENERGY EFFICIENT SCIENTIFIC
COMPUTING. HE RECENTLY LED A
MULTI-INSTITUTION DEPARTMENT OF
ENERGY OFFICE OF SCIENCE
MICROELECTRONICS CO-DESIGN
PROJECT ON NOVEL PROBABILISTIC
NEUROMORPHIC COMPUTING CALLED
COIN FLIPSS. IN 2023 BRAD WAS
RECIPIENT OF THE THE PRESTIGIOUS
PRIZE FOR NEUROMORPHIC
ENGINEERING. AND HE WAS ALSO A
NATIONAL ACADEMY OF ENGINEERING
IN THIS FRONTIERS OF ENGINEERING
INVITEE SINCE 2017. BEFORE
JOINING THE DEPARTMENT OF ENERGY
BRAD STUDIED WITH RUSTY GAUGE AT
THE SALK INSTITUTE AS
COMPUTATIONAL NEUROSCIENCE IN
NEUROGENESIS. YOU WILL HEAR A
LITTLE BIT OF THAT RESEARCH
TODAY AND E HE WILL TELL HOW
NEUROMORPHIC COMPUTING CAN HELP
US UNDERSTAND THE BRAIN. BRAD.
[APPLAUSE]
>> LET'S WELL COP OUR SPEAKER.
-- WELCOME OUR SPEAKER.
>> THANK YOU, GRACE. THANK YOU,
EVERYONE FOR THIS AMAZING
OPPORTUNITY TO COME HERE AND
SPEAK WITH YOU TODAY, I HAVE
BEEN -- IT IS QUITE AN HONOR
LOOKING AT THE PAST PRESENTERS
OF THIS LECTURE SERIES AND I
THINK IT IS -- I THINK IT IS AN
EXCITING TIME TO BE ABLE TO COME
AND SHARE SOME OF THE THINGS
THAT ARE GOING ON IN MY FIELD,
NEUROMORPHIC COMPUTING PEELED,
WHICH IS A NEW FIELD FOR ME, AS
GRACE MENTIONED, MY ORIGINAL
WORK AS A NEUROSCIENTIST WAS
LOOKING AT ADULT NEUROGENESIS, I
WILL TELL ABOUT TO START. OVER
THE LAST DECADE FOCUSED ON
COMPUTING SIDE. SOMEWHAT
SURPRISINGLY NEUROMORPHIC
COMPUTING HASN'T HAD A LOT TO DO
WITH NEUROSCIENCE HISTORICALLY
BUT IT IS CHANGING AND WE HAVE A
REAL OPPORTUNITY IF WE TAKE --
CHOOSE TO TAKE ADVANTAGE OF IT
TO BRING THESE FIELDS CLOSERRING
TO AND HELP US GAIN NEW INSIGHT
HOUSE THE BRAIN WORKS. SO I'M
GOING TO START WITH A -- KIND OF
METAPHOR IF YOU WILL OR
INSPIRATION IF YOU WILL. AND
LOOKING AT HOW MODELING AND
SIMULATION HAS HAD A TREMENDOUS
IMPACT IN OUR SOCIETY AS A
WHOLE. THIS IS HURRICANE MILTON
THAT IF YOU ARE FAMILIAR WITH
THE NEWS MOST PEOPLE ARE, HIT
TAMPA A COUPLE OF WEEKS AGO. AND
WHAT IS AMAZING ABOUT THIS IS,
WE ARE ALL FAMILIAR WITH THESE
CONES OF UNCERTAINTY WHERE THE
STORM TRACKS COME AND THIS IS
CASE THE AVERAGE PERSON IS
SEEING MODELING SIMULATION
IMPACT THEIR LIVES IN REAL TIME.
DAY TO DAY WE GET A NEW FORECAST
WHERE THE STORM MAY GO. THIS IS
AN I BELIEVE USUAL ONE, WENTS
FROM WEST TO EAST, NOT WHAT
HURRICANE DOS IN THAT PART OF
THE COUNTRY. BUT WHAT IS EVEN
MORE AMAZING, THIS STORM DID
ALMOST EXACTLY WHAT THE MODELS
PREDICTED. THAT DOESN'T ALWAYS
HAPPEN BUT IT IS HAPPENING MORE
AND MORE OFTEN BECAUSE OUR
ABILITY TO SIMULATE THESE
SYSTEMS IS COMPLEX WEATHER, INCS
GOTTEN BETTER AND BETTER EVERY
YEAR AND PART OF THE REASON WHY
IS IF YOU LOOK AT THESE SORT OF
SUPERCOMPUTERS THAT NOAA HAS, IN
VIRGINIA OR ARIZONA, THESE LARGE
SCALE GIANT HIGH PERFORMANCE
COMPUTING SYSTEMS, IN EFFECT CAN
BE DEPLOYED SAVING UNTOLD NUMBER
OF LIVES AND TREMENDOUS ECONOMIC
IMPACT. THE QUESTION, WHY ARE WE
NOT ABLE TO DO THIS TODAY FOR
MENTAL HEALTH AND NEUROLOGICAL
DISORDERS? THIS IS THE
CHALLENGE, MOTIVATING THOUGHT.
WOULDN'T IT BE NICE IF WE COULD
MAKE THAT SAME PREDICTION?
OTHERS ARE THINKING ABOUT THIS
AS WELL. BUT I WANT TO COME AT
IT FROM A DIFFERENT PERSPECTIVE.
WHICH REALLY IS LOOKING FROM THE
BOTTOM UP. AS SOMEONE WHO
MODELED BIOLOGICAL PROCESS, KIND
OF HIGH LEVEL GRANULARITY, AT
LEAST TIME, HOW DO WE GO FROM
THIS LEVEL UNDERSTANDING INTO
THIS SORT OF SYSTEM
UNDERSTANDING OF HOW THE BRAIN
WORKS AND HOW DISEASES MAY BE
UNDERSTOOD AND BE TREATED? IF WE
WANT TO START BUILDING THESE
SORT OF HIGH FIDELITY MODELS OF
NEURO-PROCESSES NEURAL MY WORK S
WELL AS BROADER COMMUNITY
STARTED HELP SHAPE A PATH
FORWARD HOW WE ARE GOING TO
POTENTIALLY WHAT WE NEED TO DO
TO GET THERE. THEN I WANT TO
TALK ABOUT THIS FIELD OF
NEUROMORPHIC COMPUTING WHICH IS
A REALLY A NEW THING I IMAGINE
FOR MANY OF YOU PROBABLY A NEW
CONCEPT, IT IS SOMETHING THAT IS
REALLY AN EXCITING AREA THAT'S
REALLY EMERGING ON THE SCENE
RIGHT NOW. AND TALK ABOUT HOW
THAT MAY ACTUALLY HAVE REAL
UNPREDICTED INSIGHTS HOW NEURAL
COMPUTATION MAY BE OCCURRING. SO
TO START, I WANT TO GO BACK TO
ADULT NEUROGENESIS, THIS IS A
PROCESS THAT IS QUITE I STILL
FIND VERY EXCITING THE IDEA THAT
RIGHT NOW ALL OF US IN THIS ROOM
ALL OF US LISTENING ONLINE, ARE
EXPERIENCING THE ADDITION OF NEW
NEURONS INTO OUR HIPPOCAMPUS NOT
A TON OF THEM MIND YOU BUT THEY
ARE HAPPENING. DEVELOPMENT DOES
NOT END WHEN YOU ARE FIVE YEARS
OLD THE WAY WE WERE TAUGHT
GROWING UP. THERE ARE NEW
NEURONS IN THE ADULT BRAIN. THIS
IS A LOCALIZED PROCESS, AS YOU
SEE HERE, KIND OF HIGHLIGHTED IN
THE DENTIGEROUS OF THE
HIPPOCAMPUS. THE BOTTOM RIGHT
IS AN IMAGE FROM (INDISCERNIBLE)
A POST DOC WHEN I WAS A GRAD
STUDENT WHO TOOK THESE BEAUTIFUL
PICTURES OF THESE GFP POSITIVE
NEURONS, THE GREEN CELLS ARE THE
NEWBORN CELLS, ABOUT A MONTH OLD
IN A MOUSE. AND IT IS
PARTICULARLY A SMALL POPULATION
OF GRANULE CELLS WHICH ARE THE
DOMINANT CELL POPULATION IN THE
DEN DAY WHICH ARE THE PINK CELLS
YOU SEE THERE. SO AS I
MENTIONED, THIS WAS KIND OF ONE
OF THESE PEOPLE LOVE TO USE THIS
AS AN EXAMPLE, ONE ICE DOGMAS
THAT WAS OVERTURNED WE KNOW
NEUROGENESIS DOES EXIST THOUGH
WE WERE TOLD IT DOES NOT. BUT IT
IS NOT A TON OF CELLS AS THEY
SAID. HUMANS MITT IT MAY BE ANY
GIVEN TIME HAVING A FEW THOUSAND
NEURONS IMMATURE CRITICAL STATE.
STILL TO BE DETERMINED. THIS IS
VERY DIFFICULT TO MEASURE IN
HUMANS FOR OBVIOUS REASONS. WE
DO KNOW THOUGH IN RODENT THIS IS
HAS A REAL IMPACT ON MEMORY
FORMATION. IT SEEMS TO BE
CLEARLY IMPORTANT FOR COGNITION.
AND WE ALSO KNOW IT IS HIGHLY
CORRELATED WITH A LOT OF THINGS
THAT ARE CLINICALLY HAVE A LOT
OF INTEREST, THINGS LIKE
EPILEPSY, DEPRESSION, STRESS,
AGING, ALL OF THOSE SEEM TO HAVE
SOME INTERACTION WITH
NEUROGENESIS AND AT THE SAME
TIME SOME THINGS LIKE SSRIs
APPEAR THE INTERACT AND TO TALK
ABOUT A LITTLE BIT IS THE
COMPUTATIONAL IMPACT OF THIS.
WHAT IS CONVENIENT ABOUT THIS
PROCESS IS THAT THIS -- WE WERE
ADDING NEW -- APPARENTLY THE
ADDITIONAL NEURONS IS OCCURRING
IN A BRAIN REGION RELATIVELY
WELL STUDIED FROM A
COMPUTATIONAL POINT OF VIEW.
DENTIGEROUS IS WHERE LTP WAS
ORIGINALLY SEEN IN VIVO IN
MAMMALS. WHAT WE -- WHAT PEOPLE
WITHIN OBSERVING WHILE I WAS A
GRAD STUDENT A LOT OF THE FIELD
WAS RESONATING AROUND THIS IDEA
YOU SEE ON THE LEFT HERE, THAT
THESE KNEW NEURONS AS THEY COME
IN -- NEW NEURONS AS THEY COME
IN OVER THE COURSE OF A MOUSE
MULTIPLE WEEKS OF DEVELOPMENT,
THAT THEY ARE -- THEY GO THROUGH
THAT'S PERIODS OF INCREASED
EXCITABILITY AND INCREASE
ABILITY TO LEARN. THEY START OFF
QUIET, THEIR STEM CELLS NOT
WIRED INTO ANYTHING, BECOME
HYPERACTIVE, THEY ARE LIKE
HYPERACTIVE LITTLE CHILDREN AS
YOU SEE ON THE VIDEO ON THE
RIGHT. AND MATURE CELLS
EVENTUALLY MATURE AND GET QUIET
AND ONLY RESPOND TO WHAT THEY
ARE SUPPOSED TO. SO THIS SORT OF
MODEL OF COMPUTATION WAS
APPEALING, IT WAS ATTRACTIVE
BECAUSE WHAT WE HAVE IS A CASE
WHERE THERE IS A CLASSIC CODING
SCHEME IN THE DENTIGEROUS WHERE
EACH NEURON RESPONDS IN EFFECT
LIKE ALMOST LIKE A GRANDMOTHER
CELL, NOT QUITE A GRANDMOTHER
CELL BUT RESPONDS HIGHLY
SELECTIVELY TO MALL SET OF
THINGS BUT YOUNG NEURONS
TRANSIENT PERIOD OF TIME ARE
RECEPTIVE TO NEW THINGS AND IN
THAT PROCESS LEARN TO RESPOND TO
THOSE LATER. BASICALLY SAMPLED
LOTS OF POSSIBILITIES AND BECOME
EXPERT IN ONE OR TWO THINGS.
THIS WAS COMPELLING AND
INTERESTING FROM COMPUTATIONAL
POINT OF VIEW. IT MAKES YOU
START THINKING WHAT DOES IT MEAN
TO BUILD A COMPUTING SYSTEM AN
ALGORITHM IF YOU WILL, THIS IS
BEFORE AI TOOK OFF. SO WHEN WE
STARTED THINKING WHAT THIS
MEANT, I WON'T GO INTO THE
MODELING ITSELF IN DEPTH BUT THE
CONCLUSION WE HAD WAS THAT
DENTIGEROUS HAS BEEN KNOWN AND
BELIEVED FOR MANY YEARS TO BE
IMPORTANT PATTERN SEPARATION THE
ABILITY TO DISCRIMINATE BETWEEN
RELATIVELY SIMILAR THINGS, YOU
CAN SEE THIS FROM ANIMAL
BEHAVIOR SIDE WHERE ANIMAL
CONTEXT FEAR CONDITIONING
DISCRIMINATE BETWEEN TWO SIMILAR
ENVIRONMENTS. DIFFERENT
POPULATIONS OF NEURONS RESPONDS
TO SIMILAR INPUTS, SO FORTH.
BUT WHAT WAS COMPLICATED STILL,
IF THE DENTY GYRUS IS GOING TO
PATTERN SEPARATION BY
CONSTRUCTION AND ANATOMICAL
REASONS TO THINK THAT BE THE
CASE, WHY WOULD YOU NEED TO HAVE
THIS COMPLICATED PROCESS OF
NEUROGENESIS TO DO THAT? YOU
DON'T NEED NEUROGENESIS TO
PATTERN SEPARA SEPARATE. THEN
ESPECIALLY GIVEN THE REST OF THE
BRAIN SHUT OFF NEUROGENESIS. THE
VAST MAJORITY OF THE BRAIN DOES
NOT HAVE NEURONS COMING IN REAL
LIFE SO CLEARLY SOMETHING MORE
HAS TO BE HAPPENING. BUT WE JUST
DIDN'T HAVE THE TOOLS TO
UNDERSTAND IT. SO REALLY, THE
CHALLENGE THAT I HAD AT THE
TIME, THIS WAS I WAS FINISHING
MY Ph.D., SOMETIMES POST DOC
AND TRYING TO UNDERSTAND IF I
HAD THIS CONCEPTUAL MODEL OF
WHAT NEW NEURONS MAYBE DOING, I
WANT TO GET TO THE -- I WANT TO
-- WE ARE IN IT TO TREAT
DISEASES AND IMPROVE COGNITION
WITH AGING AND OVERCOME EFFECTS
OF MENTAL HEALTH ON COGNITION
AND SO FORTH. WE KNOW
NEUROGENESIS IS LIKELY INVOLVED
BUT HOW DO WE CONNECT THOSE
DOTS? WE NEED TO EXPAND IN SOME
DIRECTION. AND THE TWO OBVIOUS
WAYS LOOKING AT IT FROM MY
PERSPECTIVE AS A COMPUTATIONAL
PERSON, WAS MAYBE IT IS A
CHALLENGE WITH MODELING, THAT WE
SIMPLY DON'T HAVE A BIG ENOUGH
MODEL. WE DON'T HAVE A
REALISTIC SIZE MODEL, AT THIS
TIME THIS IS BEFORE AI BEFORE
GPUs THAT RUN BILLIONS OF
NEURONS MODELS. MAYBE I NEED A
BIGGER MODEL. BUT EVEN IF YOU
LOOK AT THIS, IT IS A LOT OF
NEURONS IN OUR BRAINS, EVEN
COMPARED TO TODAY'S COMPUTING
CAPABILITIES, WE HAVE A LOOT OF
SYNAPSES, A LOT OF -- LOT OF
SYNAPSES, LOT OF TIMES MILLION
SECONDS IN A MONTH. THEN THERE'S
THE OTHER -- MILLISECONDS. THE
OTHER ISSUE ON THE RIGHT T
BIOLOGICAL COMPLEXITY IS PREMEN
DOUSE. SO MUCH WE DON'T KNOW.
ONE EXAMPLE OF WHAT WE DIDN'T
KNOW THAT WILL BE IMPORTANT IF
YOU ARE TALKING ABOUT
NEUROLOGICAL CONDITIONS, MENTAL
HEALTH IS THAT IT IS NOT JUST
GABA AND GLUTAMATE AND DONE,
THERE IS A LOT OF IMMENSE
COMPLEXITY AND NEUROMODULATORY
SYSTEMS THAT IS REALLY
IMPORTANT. SO I -- WHEN I WAS
FIRST THINKING ABOUT WHERE I
WANT TO GO AFTER MY TIME AT ECSD
AND SALK I WAS MOTIVATED AND
INSPIRED TO THINK WHAT CAN --
CAN WE START PUTTING THE
COMPLEXITY OF DOPAMINE
NOREPINEPHRINE, THESE MODULATORY
SYSTEMS CRITICAL IN COGNITION
AND AFFECT AND MOOD, THAT ARE
OFTEN IMPLICATED IN DISORDERS.
WE KNOW FROM ELECTROPHYSIOLOGY
WORK THESE ARE IMPORTANT WHEN IT
COMES TO THINGS LIKE BEHAVIOR OF
GRANULE CELLS AND INNER NEURON
POPULATIONS. ALONG WITH POST DOC
(INAUDIBLE) IN RUSTY'S LAB AT
TIME WHO LEFT AND WENT TO START
POSITION ON CHINA, I DID SOME
MODELING WORK WHERE WE PUT
DOPAMINE, VERY ABSTRACT MODEL SO
DETAILS ARE BASICALLY WHAT YOU
SEE AT THE TOP OF THE CHART,
SIMPLIFIED MODEL OF DOPAMINE
BASED ON PHYSIOLOGY RESULT SHE
WAS SEEING WHERE WE OBSERVE WHEN
YOU PUT THIN TO THE PATTERN
SEPARATION MODEL -- PUT THIS
INTO THE PATTERN SEPARATION
MODEL WE HAVE YOU SEE A TEMPORAL
ASYNCHRONY HAPPENING. IF I HAVE
A BEHAVIORAL MODEL, ADDING
NEURONS IN THERE, AND THEN I HAD
A DOPAMINE EVENT, THINGS THAT
HAPPEN BEFORE THE DOPAMINE EVENT
WERE ENCODED DIFFERENTLY THAN
THE THINGS THAT HAPPENED AFTER
THE DOPAMINE EVENT. IF YOU ARE
FAMILIAR WITH AI AND SOME OF THE
AI TECHNIQUES HAVE GONE IN
RECENT YEARS THIS IS NOT
DIFFERENT FROM REINFORCEMENT
LEARNING WHICH IS DOPAMINE
IMPLICATED IN, AND DIFFERENCE
LEARNING. THESE ARE FAIRLY
FUNDAMENTAL BUILDING BLOCKS OF
AI. SO WE FURTHER WENT AND THIS
WAS WORK YANG LINK'S LAB DID
BEHAVIORAL EXPERIMENTS AN
PHYSIOLOGY EXPERIMENT WHERE IS
YOU GIVE A MOUSE CONDENSED MILK
AND YOU CAN OBSERVE IN AFFECT
AFTER DOPAMINE EVENT OR
PRESUMABLY CONDENSED MILK
EQUIVALENT OF DOPAMINE EVENT,
INDUCES A REWARD RESPONSE,
LEARNING IS SUPPRESSED. NOT ALL
THAT DIFFERENT THAN WHAT MODEL
SUGGEST. THEN THIS WENT IN
CONTEXT FEAR CONDITIONING AND
OTHER THINGS, NOT FOR SAKE OF
TIME NOT SHOW THOSE RESULTS. I
THINK THIS WAS EXCITING BECAUSE
IT SHOWED THAT YES, THERE IS A
REAL REASON TO INCLUDE THE
COMPLEXITY AND MODULATORY
SYSTEMS IN THESE MODELS. BUT THE
PROBLEM IS, THIS IS ONE
MODULATOR WITH A LOT OF WORK
THAT WAS DONE BY NUMBER OF
EXPERIMENTALISTS ON THIS PAPER,
PNAS AT THE TIME. AND WE WERE
ABLE TO NAIL DOWN ONE
EXPERIMENT, ONE LITTLE MODEL
RESULT AND IT IS LIKE THERE IS
SO MUCH MORE. IT IS JUST -- THIS
IS CHIPPING OFF A TINY BIT AND
HOPING THAT WE HAVE TO DO THIS
FOREVER. SO THAT WAS A LITTLE
UNDERWHELMING. IT WAS EXCITING,
I LOVE THE RESULT BUT IS THIS A
PATH FORWARD TO TRANSMITTER BY
TRANSMITTER CELL TYPE BY CELL
TYPE CHIP AWAY AT WHAT IS GOING
ON. AS WAY OF FRAMING THIS, MANY
OF YOU ARE FAMILIAR WITH PAT
CHURCHHILL AND TERRY
(INDISCERNIBLE) HAD A BOOK IN
1988, TEN YEARS AGO HAD A
FOLLOW-UP PAPER LOOKING AT TIME
SCALES OF EXPERIMENTAL
MODALITIES SPATIAL SCALES AND
THIS DIFFERS TREMENDOUSLY
WHETHER DOING SINGLE SPIKES AND
PHYSIOLOGY OR FMRI IMAGING. THIS
SAME THING HAPPENS WITH
PLASTICITY. NEUROGENESIS IS
OCCURRING OF COURSE AS I
MENTIONED WEEKS AND MONTHS
SCALES, CIRCUIT SCALES WITH HOD
LAY TORRS ARE DIFFERENT TIME
SCALES. SO IN THE SENSE YOU
START LOOKING AT THIS, THE TIME
SCALES THE SPATIAL SCALES, THE
COMPLEXITY SCALES, BECOMES A
LITTLE TAUNTING. -- DAUNTING BUT
WE HAVE GIANT COMPUTERS.
SHOULDN'T THAT BE ENOUGH? SO FOR
THIS REASON, LOOK AT THAT
COMPLEXITY THINKING I'M NOT
GOING TO SOLVE THE PROBLEM ONE
SYSTEM AT A TIME, ESPECIALLY AS
A MODELER. I LOVE WORKING WITH
EXPERIMENTALISTS BUT THAT IS NOT
NECESSARILY THE BEST WAY TO
START A CAREER IS WHERE YOU ARE
DEPENDENT UPON BUNCH OF PEOPLE
TO GIVE YOU DATA. SO I WEPT TO
SANDIA NATIONAL LABS. IF YOU
ARE NOT FAMILIAR WITH DOE
LABORATORIES THEY GENERALLY
DON'T HAVE A LOT OF
NEUROSCIENCE, THAT'S BEGINNING
TO CHANGE. AT THE TIME THEY
DIDN'T HAVE MUCH AT ALL. BUT
THEY DO HAVE REALLY BIG
COMPUTERS. IN FACT THE BIGGEST
COMPUTERS IN THE WORLD RIGHT NOW
ARE OAKRIDGE NATIONAL LAB AND
ARGON NATIONAL LAB. WE ARE
GETTING TO THE 18 BLOTCHES A T A
FILE, SANDIA, THIS IS 13, 40
YEARS AGO SANDIA HAD ONE OF THE
BEGGEST SUPERCOMPUTERS AVAILABLE
TO SANDIA STAFF TO RUN
EXPERIMENTS ON. SO I WENT AND
SAID LET'S GOES TO REALISTIC
SCALE DE DENTIGEROUS SIMULATION,
MAYBE TO SOLVE THE PROBLEM. I
DIDN'T IT SEEMS TO ME THE
ORIGINAL MODEL BUT GRANULE CELLS
WITH INTERNAL CORTEX INPUTS, I
SAID LET'S LOOK AT ALL THE
DIFFERENT INTERNEURON
POPULATIONS. THERE'S MANY MORE
THAN THIS BUT THIS IS MORE THAN
YOU SEE IN MOST MODELS. SIX
DIFFERENT INNER NEURON
POPULATIONS DIFFERENT TYPES OF
CELLS, GRID CELLS AND NON-GRID
CELL INPUTS AND LOOK AT
SOPHISTICATED FUNCTION, PATTERN
SEPARATION FUNCTION BEFORE AND
ABILITY TO ENCODE INFORMATION.
WHAT WE OBSERVED, I BELIEVE THE
FIRST TIME WE SAW THIS HAS BEEN
SHOWN INTUITIVELY MAKES SENSE,
IS THAT THERE IS A REAL VALUE OF
GOING TO LARGE SCALE. THIS IS 15
YEARS AGO, 25,000 NEURON MODEL
WAS STILL AT THE TIME ONE OF THE
BIGGEST MODELS AROUND. BUT THAT
IS ONLY 10% SIZE OF MOUSE
DENTIGEROUS, FOR CONTEXT RAT --
MOUSE IS 300,000 CELLS WHICH YOU
SEE TOP RIGHT, AND HUMAN IS
ABOUT 10 MILLION CELLS. IF WE
SCALE UP ON SUPERCOMPUTER AS WE
INCREASE THE SIZE OF THE
NETWORK, WE SEE ON THE BOTTOM
RIGHT BASICALLY THE ACTIVITY OF
THE OVERALL NETWORK DECREASES.
THIS IS THERE ARE REAL REASONS
FOR THIS, LOOK AT -- THERE IS A
DIFFERENCE BETWEEN NEURON THAT
HAS 10,000 SYNAPSES IN A NEURON
THAT HAS 100 SYNAPSES OR A
THOUSAND SYNAPSES. SCALING IS
NOT LINEAR WHEN TALKING THINGS
THAT ARE HIGHLY CONNECTED IN A
COMPLEX GRAPH. A LOT OF MODELS
THAT WE HAD, THIS KIND OF IS
LIKE A WARNING SIGN, A LOT OF
MODELS THAT LOOK AT BRAIN
REGIONS AND SAY I HAVE A
THOUSAND NEURON MODEL WHATEVER
REGION, UNLESS YOUR REGION IS A
THOUSAND NEURONS, LIKE A LOCUST
AURELEUS MODEL, YOU ARE PROBABLY
GETTING BIASED RESULTS BECAUSE
THERE'S COMPLICATIONS IN
SCALING. WHAT WAS INTERESTING
THOUGH WAS THAT WHEN WE ADD NEW
NEURONS TO THIS, WE SAW THAT THE
OVERALL SCALING EFFECT DIDN'T
CHANGE MATURE CELLS BUT YOUNG
CELLS DIDN'T CARE, THE YOUNG
NEURONS WEIRDLY, THEY ONLY --
ALREADY HAVE FEWER CONNECTIONS
THAN MATURE CELLS DO, THEY ARE
SMALL NETWORK NEURONS TO BEGIN
WITH SO I THAT DIDN'T CARE THE
SIZE OF THE MODEL. BUT THE
MATURE CELLS DID. WE LOOKED AT
PATTERN SEPARATION AND
INFORMATION CODING, WE SAW THAT
IT WAS IMPROVED WITH NEW
NEURONS. SO THIS WAS EXCITING
FOR ONE REASON, WHICH I DIDN'T
REALLY GO INTO WHICH IS THAT AS
I MENTION HUMAN NEUROGENESIS IS
THOUGHT A LOW LEVEL. MUCH LOWER
THAN MICE RELATIVELY BUT THIS
WOULD SUGGEST AS YOU GO TO
LARGER SCALES YOU DON'T NEED AS
MANY NEW NEURONS. THAT WAS
QUITING. BUT WHAT WASN'T
EXCITING IS AFTER DOING ALL THAT
WORK, AND THAT TOOK A WHILE TO
BUILD THE MODEL AND RUN THESE
THINGS, I REALIZE WE DIDN'T
LEARN MUCH. THIS WAS A BIT
UNDERWHELMING AT THE TIME
BECAUSE WE WERE LOOKING -- I
REALIZED IT WAS BECAUSE WE WERE
LOOKING FOR PATTERN SEPARATION
AND WE FOUND PATTERN SEPARATION
THIS IS A CHALLENGE THAT OFTEN
HAPPENS IN NEUROSCIENCE, A BIG
CHALLENGE IN SYSTEMS AND
COMPUTATIONAL NEUROSCIENCE IN
PARTICULAR, WHICH IS THAT THE
SYSTEM, THINGS ARE SO
COMPLICATED WE CONSTRUCT OUR
EXPERIMENTS FOR HYPOTHESIS BUT
SINCE SYSTEMS ARE COMPLICATED IT
IS EASY TO FINDS VALIDATION.
CONFIRMATION BIAS VALE, IT IS
CERTAINLY IN THE CASE OF
SIMULATIONS. THINGS LIKE
FINDING THE CORRELATIONS IN DATA
IS NOT HARD. SO I WAS A LITTLE
-- THIS WAS A LITTLE
DISCOURAGING. AND IN A REAL WAY
LED TO ME KIND OF DIALING BACK A
LITTLE ON THE THE BOTTOMUP
SIMULATION EFFORT. PART OF THE
REASON WAS THAT WHEN YOU START
SEEING, ASK WHAT YOU REALLY
NEED, YOU NEED LOTS OF DATA; WE
DIDN'T HAVE THAT MUCH AT THE
TIME, GETTING IT BY DEN DAY WAS
NOT JUST THE DEN DAY, EVERYTHING
ELSE MATTERS TOO. THE TIME
SCALES ARE A BIG DEAL. AND WE
HAVE THIS BIG GROWING ISSUE THAT
I DIDN'T KNOW WHAT TO LOOK FOR
WHEN IT CAME TO COMPUTATIONAL
MODELS. LIKE WHAT IS A TASK I
WANT TO DO. CONTEXT FEAR
CONDITIONING WE SAW EFFECTS ON
IN MOUSE, IT INVOLVES A LOT OF
SYSTEMS LOT MORE IN DENTATE SO
HOW DO WE MATCH THESE? WE HAD
THESE SORT OFFER HARD LESSONS --
OF HARD LESSONS YOU CAN MODEL
SOMETHING COMPLICATED OR SMALL
UNCOMPLICATED OR BIG AND SIMPLE,
WE NEED BETTER BEHAVIORAL TASK.
THIS WAS ME ABOUT TEN YEARS AGO.
WHAT HAPPENED ABOUT TEN YEARS
AGO? I THINK EVERYONE HERE IS
FAMILIAR WITH ONE THING THAT
HAPPENED, THERE WAS A MAJOR
INVESTMENT IN THE BRAIN
INITIATIVE THAT WANTED -- I
WOULD SAY WONDERFULLY SAID LET'S
GET TO THIS BIOLOGICAL
COMPLEXITY CHALLENGE. WE DON'T
KNOW ENOUGH. I SHOWED YOU WHY
FROM ONE EXAMPLE WE DON'T KNOW
ENOUGH ABOUT ASPECT OF THE BRAIN
TO MAKE DEEPER INSIGHT. AND THE
BRAIN INITIATIVE FACES HEAD ON.
THE HUMAN BRAIN PROJECT DID THE
SAME THING AT A DIFFERENT SCALE
AND HAD A DIFFERENT EMPHASIS OF
COURSE BUT IT WAS HAPPENING
AROUND THE SAME TIME. AND
ANOTHER THING HAPPENED AROUND
THE THIS TIME WHICH IS
ARTIFICIAL INTELLIGENCE
EXPLODED. AI IS NOT BUY LOGICAL
INTELLIGENCE, THAT IS WHY IT IS
ARTIFICIAL, NOT BIOLOGICAL, WHAT
I THINK IF YOU LOOK AT WHAT
HAPPENED IN SOME WAYS IT DID THE
OPPOSITE, IT STRIPPED WHATEVER
BIOLOGICAL COMPLEXITY REMAINED
IN THINGS LIKE NEURAL NETWORKS
AND FOCUSED ON LET'S MAKING THIS
BIG AS POSSIBLE AS SIMPLE AS
POSSIBLE. BUT AT THE SAME TIME
WE ARE DOING BOTH THE AB OHIO
MAYBE THESE THINGS THINGS CAN
COMBINE AND ONE WAY THEY COMBINE
IS NEUROMORPHIC COMPUTING. THAT
CHUTELY I BELIEVE IS A PATH BY
WHICH WE CAN GET COMPLEXITY AND
SCALE TOGETHER AND THIS
SOMETHING THAT IS NOT IN LIEU OF
THESE OTHER THINGS. WE
ABSOLUTELY HAVE TO HAVE THIS
FOCUS ON BIOLOGICAL COMPLEXITY.
THE EYE FIELD IS NOT GOING
ANYWHERE. ARTIFICIAL NETWORKS
ARE THE WORLD WE LIVE IN NOW.
HOW DO WE TAKE ADVANTAGE OF
THIS? WE CAN START PUSHING TO
THE TOP RIGHT OF THIS. SO WHAT
IS NEUROMORPHIC COMPUTING? I
HAVE THROWN ET AROUND A FEW
TIMES. NEUROMORPHIC COMPUTEING
IS A FIELD EXISTED -- PARDON ME,
EXISTED SINCE THE 19 # 0s,
CARBON MEAD IS -- CARVER MEAD,
ELECTRICAL ENGINEER CRITICAL AT
STAGES OF BUILDING INTEGRATED
CIRCUITS. TOWARDS THE ENDS OF
HIS RESEARCH CAREER, HE AND GRAD
STUDENT (INDISCERNIBLE) DECIDED
TO BEGIN TO EMULATE PARTS OF
NEURAL SYSTEMS AS UNDERSTOOD IN
THE '80s IN SILICON. THAT SORT
OF IS MEAD'S VISION ORIGINALLY,
LET'S PUT WHAT WE KNOW ABOUT
DIFFERENT SENSORY SYSTEMS AND
BUILD SILICON CHIPS OUT OF IT TO
CAPTURE THAT. THAT FIELD TOOK
OFF. THERE IS A COMMUNITY OF
RESEARCHERS THEY FOCUS A LOT ON
THINGS LIKE EVENT BASE SENSORS
THAT LED TO ARTIFICIAL RETINAS
AND COCHLEAS AND SO FORTH, BASED
CAMERAS. AND ABOUT TEN YEARS AGO
THAT FIELD WAS GOING ALONG LOOK
AT ANALOG, NON-DIGITAL SILICON
ELECTRONICS, ABOUT TEN YEARS AGO
THERE WAS A RENEWAL OF INTEREST
FROM THE MOORE COMPUTER SCIENCE
COMMUNITY, PARTLY DRIVEN BY
STUFF GOING ON IN AI LOOKING
TOWARDS LARGE SCALE SYSTEMS.
BASICALLY BECAUSE THE BRAIN IS
REALLY EFFICIENT AT COMPUTING
AND THE COMPUTERS TODAY ARE NOT
EFFICIENT AT COMPUTING. IN TERMS
OF ENERGY. SO THERE ARE TWO
BROAD CLASSES OF NEUROMORPHIC
COMPUTING. I WON'T GO INTO GORY
DETAILDETAILS. WE FOCUS AT SANN
LARGE DIGITAL CMOSS TECHNOLOGY.
THESE ARE THE UNDERLYING
FABRICATION TECHNOLOGY OF A
INTEL CHIP YOU ARE SEEING BOXED
HERE BOTTOM RIGHT AT SANDIA.
THOSE CHIPS ARE THE SAME
MATERIALS AND FABRICATION IN
YOUR CELL PHONE, LAPTOP,
WHATEVER. BUT THE ARCHITECTURE
IS NOT PROGRAMMABLE THE WAY YOU
PROGRAM A NORMAL COMPUTER, IT
DOESN'T NATURALLY REPYTHON, IT
IS AN ARCHITECTURE RATHER THAT
LOOKS MORE LIKE THE BRAIN,
PROGRAM CIRCUITS OF NEURONS. THE
OTHER ALTERNATIVE IS ANALOG
SYSTEMS, I WON'T GO INTO DETAIL,
WE DO THIS WORK AT SANDIA. IT IS
FURTHER OUT, MORE CAPTURING THE
BIOPHYSIC, HAS LONGER PATH
TOWARDS SCALING, PROBABLY THE
FUTURE BUT I'M GOING TO FOCUS
MORE ON DIGITAL CMOS TECHNOLOGY.
THIS IS MORE COMPUTER ARCHITECT
OAR CENTED SLIDE, THERE'S LIST
OF THINGS FROM THE BRAIN
TRANSLATED TO ELECTRICAL
ENGINEERING OR COMPUTER
ENGINEERING CONCEPTS LIKE
SPIKING COMPLEX NETWORKS,
ABILITY OF SYNAPSES AND NEURONS
TO EXIST IN THE SAME PHYSICAL
LOCATION, LEARNING. ALL THESE
THINGS ARE WHAT THESE HARDWARE
PLATFORMS TRY TO CAPTURE. IT IS
RAPIDSLY EVOLVING FIELD. I HI
THE FULL DETAILS OF IT THEY WILL
CONTINUES TO CHANGE OVER THE
COURSE OF THE COMING YEARS AND
DECADES BUT RIGHT NOW WE CAN GET
A BILLION NEURONS ON TO EACH OF
THESE LITTLE CHIPS YOU SEE ON
THE TOP RIGHT, MILLION NEURONS
EACH OF THESE CHIPS. WE CAN GET
BILLIONS OF NEURONS IN A
PLATFORM WHICH IS ABOUT THE SIZE
OF THIS LARGE MICROWAIVE HEAR,
WE HAVE ONE AT SANDIA, THE
LARGEST NEUROMORPHIC SYSTEM THAT
IS IN OUR LAB THERE. EACH OF HE
IS CHIPS IS ONE WATT OF POWER.
THAT IS ABOUT 100TH OF CPU THAT
MIGHT BE IN YOUR LAPTOP AND
MAYBE ABOUT A THOUSANDTH OF
PRODUCTION LEVEL GPU. GIVE OR
TAKE. I WILL TALK NEUROMORPHIC,
GIVE THREE VIGNETTES THIS LAST
PART OF THE TALK. WHERE I WANT
TO SHOW WHAT NEUROMORPHIC VIEW
OF COMPUTATION CAN TELL US ABOUT
BRAIN. GIVEN THIS SORT OF
NEUROGENESIS INSPIRED VISION
BEFORE WHERE THOSE CHALLENGES
ARE. THE OBVIOUS ONE IS THAT WE
CAN GET TO THINGS AT SCALE. THAT
IS WHAT I WILL START WITH. THE
SECOND ONE IS THAT I BELIEVE IT
PROVIDES INTERESTING CONSTRAINTS
HOW TO ANALYZE DATA, THAT COMES
OUT OF LARGER NEURAL SYSTEMS
BECAUSE IN SOME LEVEL IF WE HAVE
MILLIONS OF NEURONS IN CHIP WE
ARE GOING TO ANALYZE THAT DATA
IF WE SIMULATE IT. THE FINAL
THING WHICH IS A LITTLE BIT MORE
OUT THERE, I WILL ADMIT, IS THAT
WE MIGHT BE ABLE TO ACTUALLY
UNCOVER SOME NEW DIRECTIONS TO
THINK ABOUT HOW THE BRAIN IS
WORKING, BY LOOKING WHAT THESE
CHIPS ARE GOOD AT. SO START OFF
BY TALKING A LITTLE BIT ABOUT
OUR ABILITY TO SIMULATE NEURAL
SYSTEMS AT SCALE. SO OF COURSE
THE DREAM IS IF I HAD A
CONNECTOME OF A BRAIN,
EVENTUALLY HUMAN BRAIN BUT
CONNECTOME OF WHATEVER BRAIN YOU
CARE ABOUT CAN I MODEL THAT
CONNECTOME RELATIVELY EASILY ON
A NEUROMORPHIC CHIP? CONNECTOMES
ARE LOTS AND LOTS OF NEURONS,
LOTS OF SYNAPSES. WE GENERALLY
DON'T HAVE THEM. WELL AS MANY OF
YOU KNOW, ABOUT A MONTH AGO LESS
THAN A MONTH AGO THE FLY WIRE
TEAM (INDISCERNIBLE) AND
COLLEAGUES LARGELY PRINCETON THE
BRAIN INITIATIVE HELPED FUND A
LARGE PART OF, THE TEAM
PUBLISHED A SERIES OF FIVE
PAPERS, THESE ARE NEW, I MED MY
WAY THROUGH A COUPLE OF THEM,
NOT THROUGH EVERY DETAIL YET BUT
PART OF WHAT THEY HAD IN THIS
PAPER IS A FULLOME OF A
DROSOPHILA BRAIN. THIS IS
EXCITING BECAUSE WE HAD A
CONNECTOME FOR A LONG TIME OF C
ELEGANS BUT 307 OR SO NEURONS
THAT ARE MOSTLY ANALOG IS
DIFFERENT FROM A MODEL SYSTEM
THAT IS IN THE HUNDREDS OF
THOUSANDS OF NEURONS. AND CAN DO
SOME VERY SOPHISTICATED
BEHAVIORS. SO ONE OF THE PAPERS
PUBLISHED HERE IS A BOTTLE OF
SENSORY MOTOR PROCESSING
OLFACTORY RESPONSE, ON -- THAT
IS A MODEL OF THE FULL BRAIN. SO
ADD THE CONNECTOME AND IMPLEMENT
AD MODEL OF IT. SO WE ASKED THE
QUESTION, I DIDN'T DO IT,
BECAUSE I HAVE BEEN TRAVELING
BUT COUPLE OF MY COLLEAGUES HERE
AT SANDIA, (INDISCERNIBLE), THEY
DECIDED JUST KIND OF LIKE WHAT
WE HAD THESE CHIPS, WE HAVE THIS
CONNECTOME E THEY PUBLISH
EVERYTHING WITH IT. WE NEVER
TALKED TO THE FIVE WIRE PEOPLE
THEY DON'T KNOW WE DID THIS. BUT
THEY PUBLISHED IT, IT IS THERE,
AND WE SAID LET'S TAKE YOUR
MODEL AND OR TAKE THAT MODEL AND
STICK IT THROUGH A COUPLE OF OUR
TOOLS, (INDISCERNIBLE) A SPIKING
PROGRAMMING STOOL AND STACKS
WHICH IS A LARGE SCALE
SIMULATION TOOL AND MAP ON TO
LOIHI. THEY DID, I WILL GIVE
THEM THE CREDIT HERE BUT WE
ACTUALLY WERE ABLE TO TAKE THIS
MODEL OFF THE SHELF, LITS RALLY
OFF THE SHELF, DOWNLOAD FROM GET
LAB AND JUST STICK ON TO LOIHI
AND I TOOK WORK, IT WASN'T
INSTANTANEOUS BUT WE SEE
DYNAMICS COMING OFF NEURONS IN
THE CHIPS THAT ARE LARGELY
ANALOGOUS TO WHAT WE SHOULD SEE.
THIS IS -- I MEAN QUITE
LITERALLY HOT OFF THE PRESSES.
WE HAVEN'T GONE THROUGH
VALIDATED EVERY SINGLE THING
YET, THERE'S SOME DESIGN
CONSIDERATION WE HAD TO DO BUT
THIS SAYS THESE CHIPS COMING OFF
THINGS LIKE INTEL'S LOIHI AND
SPINNAKER, SPIN CLOUD SPINNAKER
TWO CHIPS CAN RUN BIOLOGICAL
MODELS AND IT IS A QUESTION OF
ENGINEERING NOW HOW WE FIT THE
BIOLOGY DATA WE ARE PRODUCING OR
Y'ALL ARE PRODUCING TO THE CHIPS
BEING PRODUCED. SO H IS PRETTY
EXCITING. I THINK THIS WAS QUITE
EXCITING AND NEUROMORPHIC
COMPUTING HAS A REAL VALUE.
THIS IS NOT A HUGE NETWORK
NOWADAYS FOR PUT ON GPU. I DON'T
THINK THERE IS A REAL -- WE
DON'T HAVE TO HAVE NEUROMORPHIC
TO STUDY FRUIT FLY BRAINS WE MAY
FIND VALUE BUT AS WE SCALE
LARGER SYSTEMS WE WILL. SO THIS
IS -- THERE IS A PATH. SO THE
SECOND THING. SO IF YOU CAN SEE
FROM THIS LAST PLOT THERE ARE A
LOT OF NEURONS. THIS IS ONLY
PLOTTING HUNDRED THOUSAND
NEURONS. SYSTEM WHEN WE SIMULATE
ACTUAL CORTICAL NEURONS, WE DID
ONE WITH SUPERCOMPUTERS OVER 26
MILLION NEURONS USING THE STACKS
TOOL, 20 MILLION NEURONS COMING
OUT MODELING LOSE COURTSCAL
ARCHITECT COURT CAT CONNECTIONS
WE CALL THIS A MASTER PLOT IN
SYSTEMS NEURO WHERE YOU HAVE
OVER TIME LOTS OF NEURONS,
FIRING AND NOT FIRING. THIS IS
JUST LOOKS LIKE NOISE. SO WE
EFFECTIVELY CREATE AD NEW BIG
DATA PROBLEM. -- CREATED A NEW
BIG DATA PROBLEM. ONE THING THAT
WAS AN INTERESTING THOUGHT WHEN
WE PUT THESE BIG NEURAL
SIMULATIONS ON TO THESE
SUPERCOMPUTERS, THIS WAS ON THE
OAKRIDGE SUMMIT MACHINE, WE SAW
-- WE HAVE THIS TEMPTATION WE
ALL HAVE, I'M SILL LATED EVERY
NEURON AND SHOULD RECORD EVERY
NEURON WHICH CREATE AS DATA
PROBLEM BUT WHEN YOU DO THE
NEUROMORPHIC SYSTEM IF YOU
RECORD FROM EVERY NEURON FROM
EVERY SYSTEM RATION YOU LOSE THE
ADVANTAGE OF BEING NEUROMORPHIC
IF YOU HAVE TO GIGABYTES OF
DATA. SO INPUT OUTPUT IS
EXPENSIVE SO WE WANTED TO
OBSERVE A SUBSET OF ACTIVITY
WHICH IS WHAT WE DEAL WITH WITH
THE BRAIN. I CAN'T IMAGINE
FUTURE WHERE WE ARE GOING TO
RECORD THE SPIKING ACTIVITY OF
PATIENTS OF EVERY NEURON IN
PATIENT BRAIN. WE ARE ALWAYS
GOING TO BE LOOKING AT A
SUBSETS. SO WHAT DO WE DO IF
ONLY LOOK AT SUBSET? HOW DO WE
GO FROM SPIKES TO SOMETHING A
LITTLE MORE ABSTRACT? THIS IS
WORK BY MY POST DOC WHO CAME
FROM THE BIRD SONG COMMUNITY IN
GET NEAR'S LAB AT UCSD. HE HAD
THIS REALIZATION THAT AS WE ARE
LOOK AT THIS LARGE SCALE DATA WE
TENDS TO DO WHAT PEOPLE TRAINED
IN SERIAL COMPUTING PROGRAM DO,
IS THINK ABOUT CHUNK OF TIME,
WHAT SPIKING EVENTS HAPPEN
DIFFERENT PERIOD OF TIME AND
THAT SORT OF PATTERN OF TIME
PROGRESSES FORWARD, TRAJECTORY
THROUGH TIME AND SPACE. AND
WHAT BRAD'S REALIZATION WAS
THERE IS NO REASON THE BRAIN
NEEDS TO BE -- THE BRAIN IS NOT
CHUNKING THINGS UP IN TIME, TIME
IS AN ARTIFICIAL CONSTRUCT
BECAUSE THAT'S HOW WE MEASURE
AND SYSTEM LATE THINGS BUT THESE
NEURONS ARE -- THEY ARE --
SIMULATE THINGS, THEY ARE FIRING
ASYNCHRONOUSLY BASED ON THEIR
INPUTS, MAYBE A LITTLE LOOSE
INFORMATION.
THAT'S WORK LOOK AT CAUSAL
RELATIONSHIPS BETWEEN NEURONS IN
THESE MASTER PLOTS AND WHAT BRAD
REALIZED IF I'M DOING A
SIMULATION I HAVE A CONNECTOME.
I SIMULATED IT. I KNOW WHAT
CONNECTS TO WHAT. IF YOU COMBINE
THAT ASSORTMENT OF FUNCTIONAL
CAUSALITY MEASURES, THAT PEOPLE
HAVE BEEN DOING FOR A FEW YEARS,
WITH STRUCTURAL CONSTRAINTS, YOU
GET SOMETHING THAT IS ANALOGOUS
TO A SPIKE DEPENDENT PLASTICITY
ROLE, WON'T GO INTO DETAIL BUT
COMING OFF THAT WE DID SOMETHING
WE CALL GRAPHICAL NEURAL
ACTIVITY THREADS. WE LIKE
ACRONYMS. WE ARE A NATIONAL
LAB. GOT TO LOVE THE ACRONYMS SO
OUR GNATS DECOMPOSE THESE INTO
SERIES OF OVERLAPPING
POPULATIONS OF CAUSALLY LINKED
NEURON PATTERNS. WHAT'S REALLY
GREAT, THIS IS A PAPER WE HAVE
ON ARCHIVE WRITING UP FOR MORE
FORMAL WAY RIGHT NOW, IS THAT
YOU SEE THESE SAME SORT OF
CORRELATED PATTERNS CAN EXIST
SEVERAL TIMES OF THE SYSTEM, IT
IS NOT JUST A ONE OFF THING,
SEEING LIKE ENSEMBLES THAT ARE
SPATIAL TEMPORAL THAT WILL
REPEAT THEMSELVES OVER TIME.
MOREOVER, IF I WITH GIVE CERTAIN
SET OF INPUTS I SEE CERTAIN
PATTERNS RELIABLY REACTIVATE
THEMSELVES MAYBE NOT EVERY TIME,
THERE'S STOW CHASTITY IN THE
MODEL AND RANDOM WHEN THEY POP
UP BUT THIS IS A FAIRLY SCALABLE
APPROACH TO ABSTRACT AWAY NEURON
ONE FIRE AT TIME 1, NEURON 2
FIRED AT TIME 5, THIS IS ZOOMING
OUT MAYBE MAKE SOMETHING A
LITTLE MORE USEFUL FROM A
COMPUTING POINT OF VIEW,
POTENTIALLY FROM A MONITORING
POINT OF VIEW. SO THIS IS EARLY
DAYS WE JUST GOT CRC PROJECT
THROUGH DOE THAT'S WORKING WITH
TIM GETNER AT UCSD AND JOE
ROSEFELD AT USF TO LOOK AT MORE
DETAIL WITH REAL DATA BUT WE ARE
PRETTY EXCITED. GRAB SOME
WATER. THE FINAL STEP I WOULD
LIKE TO TALK ABOUT BRIEFLY, IS
THIS IDEA THAT WE MIGHT BE ABLE
TO GET A DEEPER UNDERSTANDING
ABOUT HOW THE BRAIN'S
FUNCTIONING FROM LOOKING AT WHAT
THE NEUROMORPHIC SYSTEMS ARE
GOOD AT. SO HYPOTHESIS HERE, I
WILL SAY I THINK THIS IS
SOMEWHAT A GRATIFIED HYPOTHESIS
IT IS CERTAINLY IN THE
NEUROMORPHIC FIELD.
NEUROMORPHIC COMPUTING
POTENTIALLY HELP US UNDERSTAND
THINGS THE BRAIN IS ACTUALLY
GOOD AT BY VIRTUE OF HAVING BEEN
CONSTRUCTED AT THE SAME
ARCHITECTURES AND SAME DESIGN
PRINCIPLES THAT WE BELIEVE THE
BRAIN HAS. CAN IT IN A SENSE
NEUROMORPHIC COMPUTING HELP US
GET SOMETHING BETTER THAN
PATTERN SEPARATION? SOMETHING A
LITTLE MORE COMPUTATIONALLY RICH
AND USEFUL. I WANT TO CONTRAST
THIS BY SAYING NEUROMORPHIC
EXCUSING IS NECESSARY USE AI TO
DO THIS. PART OF THE REASON IS
THAT ARTIFICIAL INTELLIGENCE
WHILE POWERFUL AS IT IS, IS NOT
CONSTRAINED BY PHYSICAL SPACE OR
BY ENERGY CONSTRAINTS, WE KNOW
IN BECAUSE WE CAN LOOK AT THE
ENERGY COST OF TODAY'S AI
ALGORITHMS IN PRODUCTION. IT IS
ALWAYS A POSSIBILITY IF I NEED
NEURAL NETWORK TO DO SOMETHING I
THROW MORE DATA AND MORE LAYERS
AND GET IT TO WORK. THAT DOESN'T
TELL US ANYTHING ABOUT HOW BRAIN
WORKS BECAUSE I THE BRAIN CAN'T
SAY I NEED TO LEARN GERMAN,
GOING THERE NEXT YEAR, ADD A FEW
MORE LAYERS TO CORTEX. THAT IS
NOT AN OPTION FOR US. OUR BRAINS
EXISTED WITH CONSTRAINTS. SO I
DON'T THINK THE FACT THAT NEURAL
NETWORKS ARE SUCCESSFUL AT DOING
COGNITIVE THINGS IS AS USEFUL AS
WE MIGHT WISH THEY WERE. I THINK
WE HAVE TO INCLUDE THE
CONSTRAINT OF EFFICIENCY. THE
REASON IS, IS BECAUSE IT IS NOT
AMOUNT OF WORK THAT AN ALGORITHM
REQUIRES BUT THE AM OF WORK
UNDER CONSTRAINT, OUR BRAIN HAVE
TO DEAL WITH THAT, THESE
NEUROMORPHIC CHIPS HAVE TO DEAL
WITH THAT. I HAVE A CONCRETE LOW
POWER DESIGNED LIKE THE BRAIN
BUT I CAN ONLY FIT SO MUCH ON
THEM. SO WHAT CAN I DO WITH
THAT? IF YOU BELIEVE THAT THE
NEUROMORPHIC CHIPS THAT WE ARE
DEVELOPING HAVE AT LEAST
OVERLAPPING SET OF CONSTRAINTS
THE BRAIN HAS, WE BELIEVE WE
MIGHT BE ABLE TO ACTUALLY MAKE
INSIGHTS ABOUT WHAT WE CAN DO.
VERY QUICKLY, THIS IS WHAT A
NEUROMORPHIC SYSTEM LOOKS LIKE.
IF YOU ARE FAMILIAR WITH FIRE
NEURON IT IS A SIMPLE MODEL OF
SPIKE NEURON, MOST NEUROMORPHIC
CHIPS HAVE HUNDREDS OF THESE
FIRE NEURONS ON INDIVIDUAL
COMPUTE CORE AND A CHIP HAS FEW
HUNDRED CORES AND THEN I CAN
WIRE UP A BUNCH OF CHIPS
TOGETHTOGETHER. A NICE HIERARCHT
I'M PROGRAMMING NEURONS TALKING
TO OTHER NEURONS SYNAPSES.
SAME AS OUR BRAIN. THE TWO
FUNCTIONS IN THE REMAINING TEN
MINUTES I WANT TO SHOW, THAT WE
HAVE DISCOVERED NEUROMORPHIC
EXUDING IS HIGHLY EFFICIENT AT.
THINGS THAT WE DID NOT EXPECT TO
BE SUCCESSFUL AT. MY DAY JOB AT
SANDIA IS TO LOOK AT
NEUROMORPHIC HARDWARE AND SAY
WHAT CAN THIS DO FOR COMPUTING
IN GENERAL. SANDIA AS A WHOLE IS
WORRIED -- DOE IS VERY WORRIED
ABOUT ENERGY COST ASSOCIATED
WITH COMPUTING TODAY. SO WHAT
CAN NEUROMORPHIC COMPUTING
COEFFICIENTLY? WE HAVE
DISCOVERED TWO THINGS. ONE IS
THAT PROBLEMS THAT REQUIRE
WIDESPREAD SAMPLING YOU MIGHT --
OUR BRAINS DO THE SYNAPTIC
SAMPLING AND PROBLEMS THAT ARE
SPATIALLY DISTRIBUTED SO
PHYSICAL SOLUTIONS TO PROBLEMS
THAT HAVE SPATIAL CONSTRAINTS.
THE FIRST THING WAS DONE
MYOPRIMARILY WILLIAM WHO DID THE
FLY WIRE MAPPING AND DARBY SMITH
WHO WAS POST DOC NOW STAFF, BOTH
USED TO BE POST DOCS NOW STAFF
AT SANDIA MATHEMATICIANS, THEY
ARE FANTASTIC TO WORK WITH, I
HIGHLY RECOMMEND IT. I MENTION
EARLIER THE HURRICANE
PREDICTION, THIS IS A DIFFERENT
HURRICANE. BUT OF COURSE THERE
ARE A LOT OF PROBLEMS IN THE
WORLD WE CARE ABOUT THAT RELY ON
UNCERTAINTY TO UNDERSTAND. OUR
COMPUTERS ARE NOT GOOD DEALING
WITH UNCERTAINTY, FLIPING A
TRANSISTER IS A BINARY DISCRETE
DETERMINISTIC EVENT SO WHAT CAN
WE MODEL THINGS PROBABILISTIC
PROBLEMS ON NEUROMORPHIC
HARDWARE. THIS IS SOMETHING
PEOPLE WOULD NEVER HAVE GUESSED
IS POSSIBLE. BUT ABOUT FIVE
YEARS AGO WE REALIZED THE PAPER
CAME OUT IN 2022, WE SAW ACTUAL
NEUROMORPHIC SCALING ADVANTAGE
FOR MODELING RANDOM WALKS. IF
YOU DON'T KNOW WHAT A RANDOM
WALK IS, IMAGINE STOCK PRICE
JITTERING UP AND DOWN, YOU MAY
GO UP OVER TIME, YOU KNOW IT
WILL GO UP EVERY TIME BUT THE
PATH IT TAKES TO GETS THERE IS
STOCHASTIC, NOT PRE-DETERMINED.
SO WONDERING HOW THE MODEL WORKS
OTHER THAN TO SAY WE WERE ABLE
TO WIRE UP POPULATIONS OF
NEURONS IN A WAY THAT REPRESENT
THE STATE SPACE OVER WHICH
RANDOM WALK HAS O OCCUR. A MONTE
CARLO PROCESS EVOLVING OVER TIME
BASICALLY BE AKIN TO A SPIKE
MOVING OVER THIS MESH OF
NEURONS. EVERY RANDOM WALKER IN
MY MODEL IS A DIFFERENT SPIKE
MOVING OVER MY CHIP. THAT IS A
RADICALLY DIFFERENT WAY THINKING
ABOUT MONTE CARLO SIMULATIONS AN
COMPUTING AND IT WORKS REALLY
WELL UNDER AMORPHIC HARDWARE.
WHAT ESSENTIALLY HAPPENS, MY
11-YEAR-OLD DID THIS IN A BLEND,
AMAZES ME, I CAN'T DO THIS IN A
BLENDER B. EVERY NEURON IN THE
SYSTEM IS BEHAVING INDEPENDENTLY
OF EVERY OTHER NEURON, MESH
POINT BEHAVING INDEPENDENTLY OF
EVERY OTHER MESH POINT SO THE
RANDOM WALKS GO IN FULL PERFECT
PARALLELISM, AND THIS TURNS OUT
A HUGE ENERGY SAVINGS. WE SAW
THIS IN OUR BENCHMARKING AT THE
TIME, YOU SEE THE NUMBER OF
WALKER PER JEWELS MEASURING
ENERGY NOT POWER SUBPOENA ORDERS
OF MAGNITUDE BETTER THAN YOU SEE
ON CPU AND GPU UNDER AMORPHIC
HARDWARE SO WENT A LITTLE SLOWER
ON THE ORIGINAL NEUROMORPHIC
SYSTEMS THE NEW SYSTEMS ARE ABLE
TO GO FASTER BUT THE POWER
ADVANTAGE IS STILL THERE. SO
THIS IS SAYING THINGS LIKE
PROBABILISTIC SAMPLING. MONTE
CARLO BASE YEN INTEREST, REAL
PROBLEMS THE WORLD NEEDS TO DEAL
WITH ARE VERY NICE NEUROMORPHIC
HARD WARE. OUR BRAIN SIMILARLY
HAVE TO DEAL WITH UNSORETY. SO
-- UNCERTAINTY. THE SECOND AREA
IS ALSO WORK WITH MY CURRENT
POST DOC BRADS THEILMAN WHO DID
THE GNATS STUFF, WE LOOK AT
SPATIALLY DISTRIBUTED PHYSICS
PROBLEM. SO IF YOU DO
COMPUTATIONAL PHYSICS, MANY
PROBLEMS CAN BE HOE DESCRIBED AS
PARTIAL EQUATION THAT LOOKS LIKE
THIS. THE WAY YOU DO THIS
CLASSICALLY IS USING ELEMENT
METHODS. IF YOU ARE NOT
FAMILIAR, IT IS BASICALLY TRYING
TO UNDERSTAND SOME PHYSICAL
DYNAMIC LIKE HEAT OR WHATEVER
OVER SOME SURFACE, EVERY POINT
ON THAT MESH, MY AREA MODELING
OVER MESH OR SOMETHING, AT EVERY
POINT I BASICALLY COMPUTE THE
PHYSICS AT THAT POINT BASED ON
THE NEIGHBORING POINTS. THIS IS
A GOLD STANDARD HOW YOU DO
THINGS LIKE TURBULENCE MODELING
OR HEAT ACTIVE DEUCE MODELING SO
FORTH -- DIFFUSION MODELING.
THAT'S A LOT OF WORK IN
SCIENTIFIC LEARNING TO
APPROXIMATE THIS BUT CAN YOU MAP
THIS ON TO NEUROMORPHIC CHIPS.
AGAIN, NO ONE ANYWHERE IN THE
NEUROMORPHIC FIELD THINKS THIS
IS POSSIBLE. BUT WE WANTED TO --
BASED ON THAT PROBABILISTIC
RESULT BEFORE CAN YOU USE SOME
SORT OF SAMPLING APPROACH TO DO
THIS? AND WHAT BRAD HAD A
REALIZATION FROM HIS
NEUROSCIENCE BACKGROUND REALIZED
IS THAT YOU CAN ACTUALLY -- IF
YOU HAVE THESE CORTICAL MODELS
WHERE YOU HAVE CLUSTERS OF
NEURONS EXCITATORY AND INHIBIT
POOTORY LOCALIZE OPTSMIZING
THEMSELVES TO REPRESENTS
SOMETHING YOU CAN WIRE THOSE UP
IN A MESH NOT UNLIKE WHAT A FINE
ELEMENT MESH SHOW AND
MATHEMATICALLY YOU CAN MAKE
THOSE RELATIONSHIPS EXPLICIT
SUCH THAT I CAN TAKE THE PHYSICS
MODEL THAT COMPUTATIONAL
PHYSICIST CARES ABOUT, I CAN
TAKE THAT A MATRIX AND I CAN
REPRESENT IT AS THE CONNECTIONS
BETWEEN THESE MESH POINTS YOU
CAN SEE HERE. THE DETAILS YOU
CAN GET -- IT CAN GET
COMPLICATED BUT IT IS NOT
REQUIRING ANY DYNAMICS OR MATH
THAT IS NOT SOMETHING
NEUROSCIENTISTS HAVE BEEN
LOOKING AT FOR YEARS, IN FACT,
IT IS RATHER NICELY PARALLEL
WITH THINGS IN LIKE MORE CONTROL
PERSPECTIVES HOW CORTICAL
CIRCUITS MAY WORK. IT SCALES
VERY NICELY ON THINGS LIKE LOIHI
2 WHICH WE HAVE BEEN DOING. IF I
HID THE LABELS IN THE BOTTOM
THERE YOU WOULDN'T BE ABLE TO
TELL WHICH WAS NEUROMORPHIC
SOLUTION AND WHICH THE GROUNDS
TRUTH SOLUTION. THIS IS
SOMETHING THAT AGAIN EVERYONE
THINKS AN AI SOLUTION OR
AMORPHIC SOLUTION HAS TO BE SOME
COURSE APPROXIMATION OF WHAT WE
CAN DO WITH FANCY CALCULATORS
AND DETERMINISTIC COMPUTING. IT
DOESN'T HAVE TO BE. TURNS OUT
THESE NEUROMORPHIC CHIPS USING
BRAIN INSPIRED ALGORITHMS, USING
NEURONS AS THE UNDERLYING
COMPUTE UNITS CAN SOLVE THESE
PROBLEMS AT LEVELS OF ACCURACY
AND PRECISION THAT THE
COMPUTATIONAL PHYSICIST MAY CARE
ABOUT. IF YOU ARE LIKE CORTICAL
SYSTEMS NEUROSCIENTIST AND LOOK
AT THE MASTER PLOT HERE COMING
OFF THE MODEL, THIS CAN BE DATA
COMING OFF OF SOME SORT OF
MULTI-ELECTRODE ARRAY OR
SOMETHING LIKE THAT. SO THIS WAS
PRETTY EXCITING BECAUSE IT TELLS
US THESE ARE PHYSICS PROBLEMS,
THESE AREN'T BIOLOGY PROBLEMS.
BUT OUR NEUROMORPHIC HARDWARE
SEEMS REALLY GOOD AT DOING THIS.
AND IF YOU STOP AND THINK FOR A
SECOND, WHAT ARE THESE PROBLEMS
REALLY DOING? TRYING TO
UNDERSTAND WHAT THE PHYSICAL
WORLD IS, BASED ON SOME PERHAPS
OBSERVATIONS BUT DEAF ANY
FACTUALLY PRIOR MODELS, AND WANT
TO MAKE PREDICTIONS OF WHAT IS
GOING TO HAPPEN? WE HAVE TO DO
THAT OUR BRAIN VERSUS TO DO THAT
AND THAT IS A REAL PROBLEM OUR
BRAINS HAVE TO SOLVE AND HAVE
TROUBLE SOLVING WHEN THINGS
START TO GO WRONG. LIKEWISE, THE
ABILITY TO MAKE DECISIONS, OFTEN
REQUIRES SAMPLING BUNCH OF
POTENTIAL OUTCOMES WHICH IS
SOMETHING THAT MEANS OUR BRAINS
REALLY NEED TO DO SOME SORT OF
SAMPLING PROCESS, PROBABILISTIC
REASONING. BASE YEN REASONING
IS SOMETHING WE LONG THOUGHT THE
BRAIN WAS DOING BUT -- P BECAUSE
YEN. . BECAUSE YEN. BASE YEN. SO
WHAT I WOULD SAY, MAYBE
CHALLENGE THE NEUROSCIENCE
COMMUNITY HERE, IS THAT INSTEAD
OF THINKING OH I THINK THE BRAIN
DOES THIS BECAUSE I STUDY
COGNITION, THIS SEEMS IMPORTANT
WHAT IS FORMULA FOR THIS, HOW
DOES THAT MAP TO THE BRAIN, MAKE
WE CAN GO THE OTHER WAY -- MAYBE
WE CAN GO THE OTHER WAY AROUND.
CIRCUITS ARE GOOD AT DOING
CERTAIN THINGS. IF WE CAN
UNDERSTAND WHAT THOSE ARE LOOK
INTO THE BRAIN TO SEE IF IT'S
SOING UP SOMEWHERE AND THERE'S A
CASE THERE WAS A BEAUTIFUL PAPER
OUT OF EUROPE A FEW YEARS AGO
LOOKING AT RANDOM TRAJECTORIES
DURING HIPPOEXAMPLE REPLAY SO
THE REPLAY EVENTS MIGHT BE A
BROWNIAN SAMPLING. AND THERE'S
CONTROL MODEL QUITE SIMILAR O
THE MODELS HERE. SO TO WRAP UP,
WE HAVE THIS CHALLENGE WHERE WE
ARE LOOKING AT THE COMPLEXITY OF
THE BRAIN, THE TIME SCALES AND
THE SIZE OF THE BRAIN AND THIS
CHALLENGE OF COMPUTATION THAT WE
ARE FACED WITH. AND WE DON'T
NECESSARILY KNOW WHAT TO DO.
WHAT I BELIEVE IS THAT
NEUROMORPHIC FIELD IS NOT THE
ANSWER HERE. WE CHUTE SLEUTHLY
HAVE TO GET MORE DATA AND WE
HAVE TO UNDERSTAND MORE
CONNECTOME WE NEEDS MORE THAN
CONNECTOMES. BUT I ALSO BELIEVE
HAVING THIS MORE BRAIN INSPIRED
COMPUTING CONSTRAINT GETTING
FROM THIS IDEA WE CAN DO
EVERYTHING BY JUST HAVING ENOUGH
LAYERS OF NEURONS ON IT BUT
RATHER CONSTRAINED TO THE
PHYSICS OF THIS EMERGING
HARDWARE, MAY GIVE US A PATH TO
DO THIS. SUPPOSE TO SAY REALISM
BUT I BELIEVE WE CAN THEN AS WE
LEARN MORE FROM BIOLOGY SIDE
COMPUTE TRAJECTORY AND WE HAD A
QUESTION AT LUNCH ABOUT HOW
NEUROMODULATORS END UP UNDER
AMORPHIC COMPUTING. CERTAINLY
NO ONE IS THINKING ABOUT THAT IN
THE NEUROMORPHIC HARDWARE SIDE
AT DEEP LEVEL BECAUSE WE DON'T
KNOW ENOUGH ABOUT
NEUROMODULATORS LIKE SEROTONIN
TO TELL THEM WHAT THEY NEED. BUT
I THINK IT IS NOT UNREASONABLE
THE FIELD IS STILL YOUNG ENOUGH
WE CAN INFLUENCE IT. WITH THAT,
I WOULD BE HAPPY TO TAKE ANY
QUESTIONS, I WOULD LIKE TO FIRST
THANK -- THIS IS LARGE GROUP OF
PEOPLE I WORKED WITH OVER THE
YEARS, FANTASTIC COLLABORATORS,
EXPERIMENTAL AND COMPUTATIONAL
MATHEMATICIANS, COMPUTER
SCIENTISTS, ALL SORTS OF PEOPLE
THANK YOU VERY MUCH.
[APPLAUSE]
>> CERTAINLY ENCOURAGE ANYONE
HERE TO USE THE MIKES. WE HAVE
ONE -- MICS. WE HAVE A QUESTION
ONLINE TO GET STARTED. THE REST
OF YOU CAN USE YOUR BRAIN AND/OR
COMPUTER TO THINK OF QUESTIONS.
SO THIS IS A GOOD ONE. MISTAKES
ARE INHERENT PROPERTY OF NEURAL
NETWORKS BASED BIOLOGICAL AND
ARTIFICIAL SYSTEMS. CAN
NEURO-SYMBOLIC BASED AI HELP TO
DECREASE NUMBER OF MISTAKES MADE
BY AI?
>> I THINK IT IS POSSIBLE. SO
NEURO-SYMBOLIC PROCESSES, IT IS
A LITTLE DIFFERENT THAN HOW I
WAS DESCRIBING THINGS BUT THE
IDEA IS THAT WE KNOW THERE ARE
TWO TYPES OF AI WHERE YOU HAVE
CLASSIC SYMBOLIC MODELS EXPERTS
CONSTRUCT AND NEURAL NETWORKS
THAT ARE POWERFUL AND COMBINE
THE TWO. AND THE HOPE IS IS THAT
THIS MIGHT PROVIDE
EXPLAINABILITY AND MORE ACCURATE
MODELING. I MIGHT TWIST THE
QUESTION AND SAY OUR BRAINS OUR
NEURAL CIRCUITS MAKE MISTAKES
FOR A REASON MANY TIMES, THAT'S
HOW WE LEARN. OUR BRAINS ARE
LIFE LONG COMPUTERS, NOT
INSTANTANEOUS COMPUTERS. WE
DEVELOP OUR NEURAL NETWORKS AS
WE HAD A TRAINING PERIODS, THEN
WE VALIDATED THEM, DEPLOY THEM
AND IDEAL I WILL THEY ARE NOT
CHANGING MUCH, OUR BRAINS HAVE
TO CHANGE CONTINUOUSLY, ADAPT TO
BOTH CHANGES INTERNAL AS WELL AS
EXTERNAL. PART OF WAY THEY ADAPT
IS THROUGH THESE MISTAKES. SO I
WOULD SAY IT IS NOT ABOUT
REDUCING ERRORS, I KNOW THAT'S
HOW WE QUANTIFY AI PERFORMANCE
BUT RATHER MAKING SURE THAT WHEN
THE SYSTEM EXPLORES POSSIBLE
ALTERNATIVES SCENARIOS. SAMPLING
IF YOU WILL, THE WAY I DESCRIBE
IT, THAT ACTUALLY DOESN'T THROW
THINGS COMPLETELY AWRY, IT
DOESN'T CRASH THE CAR IF YOU
WILL IF YOU ARE LIKE
SELF-DRIVING CAR, YOU DON'T WANT
THAT, BUT YOU WANT TO EXPLORE --
YOU HAVE TO MAKE MISTAKES, THAT
IS WHAT COGNITION IS. SO I THINK
A LITTLE BIT OF EXPLORATION AND
MISTAKES IS GOOD FOR A SYSTEM. I
HOPE THAT ANSWERS THE QUESTION.
>>
>> THANKS, REALLY NICE TALK. SO
YOU SHOWED AT SOME POINT THE
SIMULATION YOU IMPLEMENTED FROM
THE FLY WIRE DATA. AND ONE THING
THAT WAS IMPRESSIVE IS IT WAS --
LOOKED LIKE REALLY LARGE NUMBER
OF NEURONS. THE FLY, THE FLY
DATA IS LARGE NUMBER. SO I'M
INTERESTED IN TRYING TO --
WONDERING WHAT ARCHITECTURE THAT
USES. ARE YOU IMPLEMENTING THE
SORT OF ANALOG INTEGRATION
DIRECTLY ON CHIP? AND THEN I
GUESS MORE BROADLY I'M
INTERESTED IN WHERE YOU SEE THE
LARGE-SCALE OF NEUROMORPHIC
COMPUTING GOING. IS ONE OF THE
-- I DON'T KNOW PEOPLE SAY THAT
OUR BRAIN WORKS BETTER THAN CHAT
GTP ON LIKE HAMBURGER EVERY FEW
HOURS AND LESS POWER THAN AI USE
BUS ONE OF THE THINGS WE ARE
THINKING IN THE FUTURE, IS BEING
ABLE TO DO REALLY LARGE SCALE
COMPUTATION IN THIS ANALOG
FORMAT USING RELATIVELY LOW
POWER. SO WONDERING WHERE YOU
THINK SCALE IS GOING AND BEING
ABLE TO SCALE TO LARGE ENOUGH
UNITS AND HOW WILL THAT HAPPEN?
>> YEAH. SO IF I FORGET THE
SECOND QUESTION REMIND ME.
QUICKLY ON THE FLY WIRE WORKS.
SO THIS IS HOT OFF THE PRESS. WE
HADN'T FULLY TESTED EVERYTHING
ON IT. THE PAPER JUST CAME OUT A
FEW WEEKS AGO. BUT WHAT WAS NICE
IS THE PAPER THAT WE ARE BASED
ON IS NOT JUST THE CONNECTOME,
IT IS ACTUALLY A PAPER DESCRIBED
THE COMPUTATIONAL MODEL FROM THE
CONNECTOME. THAT MODEL WAS
WRITTEN IN BRIAN WHICH IS A
SPIKING NEURAL NETWORK MODELING
TOOL WHICH EFFECTIVELY IS DOING
LEAKY INTEGRATED FIRE NEURONS.
THEY ARE BIT MORE COMPLEX BUT
NOT TREMENDOUSLY SO. SO WE HAVE
-- WHAT WAS NICE IS THAT MODEL
BASICALLY WE CAN TAKE DIRECTLY
FROM THE CODE AND TRANSLATE IT
ON TO WHAT THE CORE NEURAL
DIGITAL NEURAL MODELS ARE THAT
ARE IMPLEMENTED BY THE LOIHI
TOOL CHIP. SO THEY ARE NOT
ANALOG, IT IS FULLY DIGITAL, IT
IS BASICALLY THE WAY THAT CHIP
WORKS IS THAT IT HAS 100 SOME
ODD CORES THAT ARE EACH CAPABLE
OF MODELING ABOUT A THOUSAND
NEURONS EACH. IN PRACTICE YOU
GET A FEW HUNDRED NEURONS EACH
CORE. SO IT WAS MORE OR LESS JIG
SAW PUZZLE. WE WANT TO TAKE THAT
MODEL THAT HAD 125,000 NEURONS,
SPLIT IT UP INTO PIECES AN
DISTRIBUTE OVER THE LOIHI SYSTEM
THAT WE HAVE. SO THAT WASN'T --
WHAT IS NICE IS, WE DIDN'T DO
THE WORK. I DON'T WANT TO TAKE
CREDIT FOR THAT. THAT WAS THE
WORK OF THE FLY WIRE TEAM MAKING
THAT MODEL WE SHOW YOU CAN PUT
THAT TO NEUROMODULATOR. THE
SECOND IS WHERE IS THIS GOING TO
SCALE AND WHERE IS IT GOING TO
GO? DEPENDS ON IF YOU WANT TO DO
MORE BRAIN SIMULATION STUFF. OR
IF YOU WANT TO GO THIS AI ROUTE
I DON'T -- I THINK THE BRAIN
SIMULATION SIDE WE WILL HAVE A
PACKET. THERE'S ENGINEERING
CONSTRAINTS, NUMBER OF SYNAPSES
PER NEURON IS BOUND BY LOCAL
MEMORY AVAILABLE PER CORE, THINK
ABOUT SOMETHING LIKE LOCUST
ARCURELIUS NOREPINEPHRINE
NEURON, THAT IS A BILLION
NEURONS IN THE BRAIN. IN THAT
STRUCTURE WE HAVE TO FIGURE HOW
TO MAP THAT TO IF PHYSICAL
HARDWARE BUT THAT IS GOING TO
TAKE WORK, IT IS POSSIBLE AND I
THINK WE CAN PROBABLY GET TO
REALISTIC SIZE, WE HAVE A
BILLION NEURON SYSTEM TODAY THAT
WE ARE TRYING TO FIGURE HOW TO
BUILD THINGS UP ON SO
LIMITATION, THERE ARE
LIMITATIONS BUT NOTHING
FUNDAMENTAL THERE. WHEN IT COMES
TO LARGE LANGUAGE MODELS I WOULD
SAY THE UNDERLYING STRUCTURE OF
THOSE MODELS IS NOT VERY
BIOLOGICBIOLOGICAL. IT MIGHT INE
THINGS THAT ARE NEURONS AND
WEIGHTS BUT THOSE MODELS IN MANY
WAYS ARE NOT GOOD FITS FOR
NEUROMORPHIC HARD WARE. I WOULD
SAY -- PEOPLE ARE TRYING TO DO
THIS, I'M CHEERING THEM ON, I'M
NOT INCREDIBLY OPTIMISTIC THAT
WE ARE GOING TO PUTS CHAT GPT ON
TO LOIHI 2 ANY TIME SOON. WHAT I
THINK WE MIGHT BE ABLE TO DO IS
THIS SEE WHAT THESE CHIPS ARE
REALLY GOOD AT, MIGHT HELP
CONSTRAIN US TO THINK ABOUT WHAT
A DIFFERENT FORMULATION OF A
LARGE LANGUAGE MODEL SHOULD BE,
DIFFERENT FORMULATION OF LARGE
LANGUAGE MODEL SHOULD BE AND
THAT MIGHT HELP US REDESCRIBE IN
A WAY THAT IS ENERGY EFFICIENT
WHICH IS A VERY INTERESTING
SCIENTIFIC QUESTION BUT IT IS --
I DON'T HAVE AN IMMEDIATE
ANSWER. THANKS.
>> WE HAVE ONE MORE QUESTION.
>> SO AS YOU SAID WE LEARN FROM
OUR ERRORS. WHEN DO YOU THINK
YOU WILL HAVE NEUROMORPHIC
HARDWARE THAT WILL LOOK
SOMETHING LIKE CHAT GPT AND
REPROGRAM ITSELF?
>> IT IS A FUN QUESTION. I THINK
IF IT IS FULLY PROGRAMMING
ITSELF I DON'T KNOW IF WE ARE
ANYWHERE CLOSE TO THAT. I DO
THINK WE WANT TO HAVE LEARNING.
IN FACT THESE CHIPS DO HAVE THE
ABILITY TO LEARN. WE ARE -- NONE
OF THE ALGORITHMS REALLY USE
THAT. WE MIGHT HAVE CHIPS OR
MIGHT HAVE ALGORITHMS, NOT TOO
DISTANCE FUTURE THAT ARE
HOPEFULLY VERY BRAIN INSPIRED
THAT ARE INTRINSICALLY LEARNING
SO THE MODELS PUT ON TO THE
CHIPS WILL ADAM DEPARTMENT
THEMSELVES OVER TIME. SO NOT
LEARNING THE SAME AS FPGA
RECONFIGURES THEMSELVES. THEY
ARE SIMILAR TO FPGAs IN TERMS
HOW WE PROGRAM AND THINK ABOUT
THEM SO WHAT YOU ARE SUGGESTING
IS NOT IMPOSSIBLE BUT JUST NOW
HOW WE ARE REALLY THINKING ABOUT
IT.
>> SO NEUROMORPHIC PART, YOU
HAVE SPIKING, RIGHT?
>> YES.
>> BUT JUST THIS MONDAY I HEARD
A TALK BY SOMEONE WHO USED THE
CONNECTOME FROM THE FLY WIRE AND
IN THAT SHE SHOWED THAT THERE'S
A SINGLE SPIKE THAT IS INDUCED
THAT LEADS TO A SPECIFIC
BEHAVIOR IN THE FLY. SO THAT IS
VERY FAR FROM PROBABLEI PROBABIO
CURIOUS YEAH IT CAN SOLVE RANDOM
WALKS, A LOT OF CENTRAL LIMITS
HERE IS REALLY HELPFUL BUT FOR
SUCH SINGLE SPIKE EVENTS, THAT'S
HARD TO GET.
>> I WILL SAY THESE CHIPS ARE
RATHER CONVENTIONAL UNDER THE
HOOD. THESE ARE DIGITAL CMOS
ELECTRONICS. IF I WANT TO TURN
OFF NOISE I CAN TURN OFF NOISE
ON THE CURRENT HARDWARE. I DO
THINK THAT THE FLY BRAIN AND
MAMMALIAN BRAINS ARE DIFFERENT
IN TERMS OF -- I'M NOT AWARE IN
THE MAMMALIAN BRAINS WHERE YOU
HAVE SINGLE NEURONS THAT HAVE
FULLY DETERMINISTIC BEHAVIOR THE
WAY THAT PERFECTLY WELL DEFINED
CONNECTOMES LIKE THE FLY
CONNECTOME IS VERY STRUCTURED.
WHEREAS OUR BRAINS ARE MUCH MORE
RANDOMLY CONNECTED. I DO THINK
THERE IS FUNDAMENTAL QUESTION
BUT THOSE ARE MORE COMPUTATIONAL
NEUROSCIENCE QUESTIONS. WE CAN
PROBABLY IMPLEMENT EITHER
VERSION IN NEUROMORPHIC. BUT
THAT SOFTER NEURON IS THE H 1 --
I CAN REMEMBER, IT'S BEEN
STUDIED A WHILE IN FLIES. IT
WILL BE INTERESTING TO LOOK IN
-- WE HAVEN'T PLAY WITH THE
MODEL TOO MUCH TO BE HONEST BUT
TO SEE HOW THAT WORKS.
>> THANK YOU.
>> JUST QUICKLY, GREAT TALK,
GREAT TO SEE THE FLY WIRE
IMPLEMENT SOD QUICKLY. THE BRAIN
INITIATIVE IS SELECTING
COLLECTING OTHER TYPES OF DATA
INCLUDING CELL TYPE DATA DATA. E
RELEASED A WHOLE MOUSE BRAIN
CELL TYPE ATLAS, MULTIPLE
THOUSANDS OF DIFFERENT TYPES OF
CELLS. SO THE CONNECTOME IS NOT
JUST HOMOGENOUS CONNECTION
GRAPH, IT IS CONNECTIONS BETWEEN
DIFFERENT TYPES OF CELLS WITH
DIFFERENT RESPONSES, DIFFERENT
RESPONSES TO NEUROMODULATORS,
ALL THAT COMPLEXITY YOU ARE
TALKING ABOUT. WHAT DO YOU SEE
AS FAR AS WHETHER -- WELL, HOW
CAN YOU INTEGRATE THAT KIND OF
DATA ACROSS DIFFERENT SCALES,
CELL TYPE CONNECTIVITY AND HOW
DOES THAT RELATE TO THE MAJOR
OBSTACLES YOU SEE MAJOR ERROR UP
TO THE RIGHT OF THAT COMPLEXITY
VERSUS SIZE PLOT. AND HOW IT
RELATES?
>> I LOVE THAT QUESTION BECAUSE
I -- PUT THIS IN MY ORIGINAL
DENTE MODELING STUFF,
HETEROGENEITY IS FUNDAMENTAL HOW
THE BRAIN FUNCTIONS AND CRITICAL
HOW DISEASE INTERACTS WITH
BRAIN. I IT IS NOT WHAT
NEUROMORPHIC COMPUTING IS -- WE
HAVE DONE A LOT OF NEUROMORPHIC
BUT A LOT OF NEUROMORPHIC
SYSTEMS CANNED HANDLE THAT, BUT
JUST HAVEN'T DONE IT YET.
SOMETHING GPUs ARE TERRIBLE
HETEROGENEITY THAT'S PLACE
NEUROMORPHIC HAS ADVANTAGE BUT
THERE'S REAL WORK THAT WILL BE
NEEDED TO DIVE INTO SOME OF THIS
DATA AND START TO REALLY KICK
THE TIRES ON WHAT WE CAN DO SO A
FANTASTIC DIRECTION TO GO.
>> THANK YOU SO MUCH, BRAD, FOR
TELLING US ABOUT HOW
NEUROMORPHIC CAN HELP US
UNDERSTAND NEUROSCIENCE AND
THERE IS PA WALS RECEPTION RIGHT
NOW. SO LET'S GIVE A HAND TO OUR
SPARE.