Toward Tractable AGI: Challenges for System Identification in Neural Circuitry

Toward Tractable AGI: Challenges for Toward Tractable AGI: Challenges for System Identification in Neural CircuitrySystem Identification in Neural Circuitry

Randal A. KoeneCarboncopies.org & NeuraLink Co.

AGI-12, Winter Intelligence Conference 2012, Oxford

Interfacesprostheses

Reconstructionproject

Specialtools

Specific System Identification Problems

Tractable AGI through System Identification in Neural Circuitry

Representations and ModelsBehavior of interest; Signals of interest; Discovering the transfer function

Mental Processes and Neural Circuitry: Brain Emulation

System identification in neural circuitry

Simplification of an Intractable System into a Collection of System Identification Problems

Tools for structural decomposition; Data from structure; Parameter tuning among connected systems; Tools for characteristic reference recordings

ChallengesSignals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept

Tractable AGIChallenges in ourenvironment

Legg, Hutter

Optimal bounded­lengthspace­time embeddedagent Orseau

Theoretically soundAGI

Practical feasibility

Short-cuts

Brain-like AGI

Abstraction level special case:Neuronal circuitry/physiology(100 years of grounding)

Our reverse interests: Taking a niche systemand making it more adaptable, more general

Tractable AGI through System Identification in Neural Circuitry

Representations and ModelsBehavior of interest; Signals of interest; Discovering the transfer function

Mental Processes and Neural Circuitry: Brain Emulation

System identification in neural circuitry

Simplification of an Intractable System into a Collection of System Identification Problems

Tools for structural decomposition; Data from structure; Parameter tuning among connected systems; Tools for characteristic reference recordings

ChallengesSignals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept

Representations and Models

Modern science:

Observed effects

Model (testing)

Understanding

Pieces of natural environment

Not independent!

Signals, information, P(x)

described as

improve

May seem obvious to comp.neurophys. modelers... but consider whole problem not only typical solutions

Behavior of Interest

Lots of piecesSystematic modeling

Keep it simple

Interesting effectFocus, constrain scope/model

Neuroscience:Effect = Behavior(e.g. object recognition)

Signals of Interest

How do pieces communicate?

Signals

Physics: 4 interactions (gravity, electromagnetism, weak & strong nuclear force)

Constrain

Neurons: current, temperature, pressure, EM, etc…

Priority of interest: empirical (noise, predictive value)

Discovering the Transfer FunctionSI in Control Theory: Black/gray box

State, input, output

Find: Transfer Function

Formal methodsE.g. Volterra series expansion

kernels & history of input

Tractable AGI through System Identification in Neural Circuitry

Representations and ModelsBehavior of interest; Signals of interest; Discovering the transfer function

Mental Processes and Neural Circuitry: Brain Emulation

System identification in neural circuitry

Simplification of an Intractable System into a Collection of System Identification Problems

Tools for structural decomposition; Data from structure; Parameter tuning among connected systems; Tools for characteristic reference recordings

ChallengesSignals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept

Mental Processes and Neural Circuitry: Brain Emulation

Effects = Experiences

Perception, learning & memory, goal directed decision making, emotional responses, consciousness, language, motor

Observable / internal

Involves Involves ensemblesensembles of of neurons in a circuit neurons in a circuit layoutlayout

Reconstruction vs. Abstraction: Interfaces & Prostheses100 years of component level neuroscience

Individual differences

Matter to interfaces

Matter to prostheses

System Identification in Neural Circuitry

Signals of interestChip – “bits”

Initial assumptions, reliable neural communication

Sensory, muscle, learning – “spikes”

Example methods:Berger chip(Volterra exp.)

Aurel A. Lazar: Channel Identification Machines

(CNS2012 workshop on SI)

Tractable AGI through System Identification in Neural Circuitry

Representations and ModelsBehavior of interest; Signals of interest; Discovering the transfer function

Mental Processes and Neural Circuitry: Brain Emulation

System identification in neural circuitry

Simplification of an Intractable System into a Collection of System Identification Problems

Tools for structural decomposition; Data from structure; Parameter tuning among connected systems; Tools for characteristic reference recordings

ChallengesSignals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept

Simplification of an Intractable System into a Collection of System Identification Problems

SI of observable + internal = intractable if black box is brain

Many communicating black boxes with accessible I/O

Communication = note locations, trace connectivity (“Connectomics”)

E.g. compartmental modelingBriggman et al & Bock et al, Nature 2011

Characteristic responsesADP, AHP, AMPA, s/fNMDA

Whole Brain Emulation: A Roadmap to data acquisition & representation

Break into parts.How can the parts communicate?

Characterize the parts

Platform suitingrepresentation

IteratingImproving theOther pillars

(See earlier presentations, carboncopies.org.(See earlier presentations, carboncopies.org.More about roadmap & projects – leading upMore about roadmap & projects – leading upTo Global Future 2045 Congress NYC, June 15-16.)To Global Future 2045 Congress NYC, June 15-16.)

Tools for Structural Decomposition

Voxel geometric decomposition (e.g. MRI)

Cell body locations & functional connectivity

Zador RNA/DNA tags

Stacks of EM images (Denk, Hayworth, Lichtman)

Data from Structure

SI for compartmentsElectric circuit analogy

3D shapeConductance, class, etc.

“Invisible” parameters? Measurement reliability?

Parameter Tuning among Connected Systems: Reference Points

Parameters – sensible collective behavior

Reference points: constrain & validate

Resolution of reference points – combinatorial size of SI problem

# and duration of measurements(purposely abstract:

- resolutions reference/SI decomposition- not one path (e.g. Briggman et al.= problem specific criteria, not method specific – compare & collaborate projects)

Tools for Characteristic Reference RecordingsLarge arrays of recording electrodes + optogenetic selectivity

Microscopic wireless probes

Molecular “ticker-tape” by DNA amplification

Tractable AGI through System Identification in Neural Circuitry

Representations and ModelsBehavior of interest; Signals of interest; Discovering the transfer function

Mental Processes and Neural Circuitry: Brain Emulation

System identification in neural circuitry

Simplification of an Intractable System into a Collection of System Identification Problems

Tools for structural decomposition; Data from structure; Parameter tuning among connected systems; Tools for characteristic reference recordings

ChallengesSignals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept

ChallengesGeneral SI problems

Particular to neurons & neural models

Unique to pieces of neural tissue & large neuronal circuits

Exclusive to whole brain circuit reconstruction

Integration of data from structure & function acquisition tools

Care about signalsContributions outside spiking domain?

Other cells?

Neuron-neuron effects without spiking?

Evidence of sensations retained?

Assume: spikes = currency of sensations

Not epiphenomenal! (test?)

Predicting spikes

Observe / deduce spike times original system

Additional information aids prediction

What information do the tools give us?

Izhikevich

MRI

Large volumes

Not parameter tuning

But system validation!Distribution

Propagation

Requires spatial registration

3D reconstructions at 5nmGeneral classification (e.g. pyramidal vs interneuron)

Detailed morphology, segmented into compartments

E.g. radius – resistance, capacitance

Depends on neuron type

Measurement reliability, cumulative

Plasticity & Morphology

Learning changes synapses & connectome

Deformation changes morphology

3D snapshot cannot capture temporal dynamics of memory

Ticker-Tape DataMany neurons, several tapes per neuron

Time stamps + spike / membrane potential samples

Recovery of DNA snippetsNot combinable with EM

Interference with cell mechanisms

Spatial registration:Which part of ultrastructure did it come from?

Optical Functional

Calcium / proteins, fluorescent

Large scale / whole brain access?

Methods disturb tissue

Huge electrode arrays also disturb tissue

Microscopic wirelessPower & data volumes compete with continuous sampling

When enough sporadic data?

Long term dynamicsDemands frequent spatial registration

EM registration in tissue

Ongoing collaboration (MIT, Harvard)

Sufficient Data

Spike times, EFPs, membrane potentials – rate, duration?

Response shape sufficient?

Stimulate combinations?

Learning from virtual systems

NETMORPHAcquire structure data

Acquire functional data

Test algorithms & iteratively improving constraints

Calculate abstract boundary conditions?

Netmorph.org

Proof of Concept: Starting Small

Test process in small system

C.Elegans (Dalrymple)

Retina (Briggman)

Hippocampal neuroprosthetic (Berger)

Cerebellar neuroprosthetic (Bamford)

Memory from piece of neural tissue (Seung)

Discussion

Good gage of problems – proof of concept!

SI is not new! Many fields can contribute

Tools = problem 1, turning data into model = problem 2

True effort underway – seeking input from SI experts!

Thanks

Ed Boyden (MIT)Yael Maguire (MIT)Konrad Kording (NW)Ken Hayworth (JF)Many others in the WBE group! Carboncopies.org

2045.com