Toward Tractable AGI: Challenges forSystem Identification in Neural Circuitry Randal A. Koene Carboncopies.org & NeuraLink...
Tractable AGI through System Identification inNeural CircuitryRepresentations and Models  Behavior of interest; Signals of...
Tractable AGI                                         Challenges in our                                         environmen...
Tractable AGI through System Identification inNeural CircuitryRepresentations and Models  Behavior of interest; Signals of...
Representations and Models                                                           Modern science:                      ...
Behavior of InterestLots of pieces   Systematic modeling   Keep it simpleInteresting effect   Focus, constrain   scope/mod...
Signals of Interest              How do pieces              communicate?                 Signals              Physics: 4 i...
Discovering the TransferFunctionSI in Control Theory:Black/gray box  State, input, output  Find: Transfer  FunctionFormal ...
Tractable AGI through System Identification inNeural CircuitryRepresentations and Models  Behavior of interest; Signals of...
Mental Processes and NeuralCircuitry: Brain Emulation                Effects =                Experiences                 ...
Reconstruction vs. Abstraction:Interfaces & Prostheses100 years ofcomponent levelneuroscienceIndividualdifferences  Matter...
System Identification in Neural          Circuitry                                                 Signals of interest    ...
Tractable AGI through System Identification inNeural CircuitryRepresentations and Models  Behavior of interest; Signals of...
Simplification of an Intractable System into aCollection of System Identification Problems                           SI of...
Whole Brain Emulation: A Roadmap to data         acquisition & representation                                             ...
Tools for StructuralDecomposition                  Voxel geometric                  decomposition (e.g. MRI)              ...
Data from StructureSI for compartments  Electric circuit analogy3D shape  Conductance, class, etc.“Invisible” parameters?M...
Parameter Tuning among Connected Systems:            Reference Points                                                  Par...
Tools for CharacteristicReference RecordingsLarge arrays ofrecording electrodes+ optogeneticselectivityMicroscopic wireles...
Tractable AGI through System Identification inNeural CircuitryRepresentations and Models  Behavior of interest; Signals of...
Challenges             General SI problems             Particular to neurons &             neural models             Uniqu...
Care about signalsContributions outsidespiking domain?  Other cells?  Neuron-neuron effects  without spiking?  Evidence of...
Predicting spikes              Observe / deduce              spike times original              system              Additio...
MRILarge volumesNot parameter tuningBut system validation!  Distribution  PropagationRequires spatialregistration
3D reconstructions at 5nm             General classification      (e.g.             pyramidal vs interneuron)             ...
Plasticity & MorphologyLearningchangessynapses &connectomeDeformationchangesmorphology3D snapshotcannot capturetemporaldyn...
Ticker-Tape Data             Many neurons, several tapes             per neuron             Time stamps + spike /         ...
Optical FunctionalCalcium / proteins,fluorescent  Large scale / whole  brain access?Methods disturbtissueHuge electrodearr...
Microscopic wireless             Power & data volumes             compete with continuous             sampling            ...
Sufficient DataSpike times, EFPs,membranepotentials – rate,duration?Response shapesufficient?Stimulatecombinations?
Learning from virtual systems                 NETMORPH                   Acquire structure data                   Acquire ...
Proof of Concept: Starting SmallTest process in small system  C.Elegans (Dalrymple)  Retina (Briggman)  Hippocampal neurop...
DiscussionGood gage of          Tools = problem 1,problems – proof of   turning data intoconcept!              model = pro...
ThanksEd Boyden (MIT)Yael Maguire (MIT)Konrad Kording(NW)Ken Hayworth (JF)Many others in theWBE group!           Carboncop...
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Toward Tractable AGI: Challenges for System Identification in Neural Circuitry

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This is the presentation I gave at AGI-12 (also called the Winter Intelligence 2012 conferece) in Oxford, UK, on Dec.11, 2012. There is an AGI-12 proceedings paper that accompanies this talk. I will make that available on my publications page at http://randalkoene.com and I will put both together on the http://carboncopies.org page about this event. The video (recorded by Adam Ford) should also appear soon.

Abstract. Feasible and practical routes to Artificial General Intelligence involve short-cuts tailored to environments and challenges. A prime example of a system with built-in short-cuts is the human brain. Deriving from the brain the functioning system that implements intelligence and generality at the level of neurophysiology is interesting for many reasons, but also poses a set of specific challenges. Representations and models demand that we pick a constrained set of signals and behaviors of interest. The systematic and iterative process of model building involves what is known as System Identification, which is made feasible by decomposing the overall problem into a collection of smaller System Identification problems. There is a roadmap to tackle that includes structural scanning (a way to obtain the “connectome”) as well as new tools for functional recording. We examine the scale of the endeavor, and the many challenges that remain, as we consider specific approaches to System Identification in neural circuitry.

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Toward Tractable AGI: Challenges for System Identification in Neural Circuitry

  1. 1. Toward Tractable AGI: Challenges forSystem Identification in Neural Circuitry Randal A. Koene Carboncopies.org & NeuraLink Co. Interfaces Reconstruction prostheses project Special Specific System tools Identification Problems AGI-12, Winter Intelligence Conference 2012, Oxford
  2. 2. Tractable AGI through System Identification inNeural CircuitryRepresentations and Models Behavior of interest; Signals of interest; Discovering the transfer functionMental Processes and Neural Circuitry: BrainEmulation System identification in neural circuitrySimplification of an Intractable System into aCollection of System Identification Problems Tools for structural decomposition; Data from structure; Parameter tuning among connected systems; Tools for characteristic reference recordingsChallenges Signals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept
  3. 3. Tractable AGI Challenges in our environment Theoretically sound AGI Practical feasibility Short-cuts Brain-like AGILegg, HutterOptimal bounded­lengthspace­time embeddedagent Orseau Abstraction level special case: Our reverse interests: Taking a niche system Neuronal circuitry/physiology and making it more adaptable, more general (100 years of grounding)
  4. 4. Tractable AGI through System Identification inNeural CircuitryRepresentations and Models Behavior of interest; Signals of interest; Discovering the transfer functionMental Processes and Neural Circuitry: BrainEmulation System identification in neural circuitrySimplification of an Intractable System into aCollection of System Identification Problems Tools for structural decomposition; Data from structure; Parameter tuning among connected systems; Tools for characteristic reference recordingsChallenges Signals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept
  5. 5. Representations and Models Modern science: Observed effects described as Model (testing) improve Understanding Pieces of natural environment Not independent! Signals, information, P(x)May seem obvious to comp.neurophys. modelers... but consider whole problem not only typical solutions
  6. 6. Behavior of InterestLots of pieces Systematic modeling Keep it simpleInteresting effect Focus, constrain scope/modelNeuroscience:Effect = Behavior(e.g. object recognition)
  7. 7. 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)
  8. 8. Discovering the TransferFunctionSI in Control Theory:Black/gray box State, input, output Find: Transfer FunctionFormal methods E.g. Volterra series expansion kernels & history of input
  9. 9. Tractable AGI through System Identification inNeural CircuitryRepresentations and Models Behavior of interest; Signals of interest; Discovering the transfer functionMental Processes and Neural Circuitry: BrainEmulation System identification in neural circuitrySimplification of an Intractable System into aCollection of System Identification Problems Tools for structural decomposition; Data from structure; Parameter tuning among connected systems; Tools for characteristic reference recordingsChallenges Signals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept
  10. 10. Mental Processes and NeuralCircuitry: Brain Emulation Effects = Experiences Perception, learning & memory, goal directed decision making, emotional responses, consciousness, language, motor Observable / internal Involves ensembles of neurons in a circuit layout
  11. 11. Reconstruction vs. Abstraction:Interfaces & Prostheses100 years ofcomponent levelneuroscienceIndividualdifferences Matter to interfaces Matter to prostheses
  12. 12. System Identification in Neural Circuitry Signals of interest Chip – “bits” Initial assumptions, reliable neural communication Sensory, muscle, learning – “spikes”Example methods:Berger chip Aurel A. Lazar: Channel Identification Machines(Volterra exp.) (CNS2012 workshop on SI)
  13. 13. Tractable AGI through System Identification inNeural CircuitryRepresentations and Models Behavior of interest; Signals of interest; Discovering the transfer functionMental Processes and Neural Circuitry: BrainEmulation System identification in neural circuitrySimplification of an Intractable System into aCollection of System Identification Problems Tools for structural decomposition; Data from structure; Parameter tuning among connected systems; Tools for characteristic reference recordingsChallenges Signals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept
  14. 14. Simplification of an Intractable System into aCollection 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 modeling Briggman et al & Bock et al, Nature 2011 Characteristic responses ADP, AHP, AMPA, s/fNMDA
  15. 15. Whole Brain Emulation: A Roadmap to data acquisition & representation Characterize the partsBreak into parts.How can the parts communicate? Platform suiting representationIteratingImproving theOther pillars (See earlier presentations, carboncopies.org. More about roadmap & projects – leading up To Global Future 2045 Congress NYC, June 15-16.)
  16. 16. Tools for StructuralDecomposition Voxel geometric decomposition (e.g. MRI) Cell body locations & functional connectivity Zador RNA/DNA tags Stacks of EM images (Denk, Hayworth, Lichtman)
  17. 17. Data from StructureSI for compartments Electric circuit analogy3D shape Conductance, class, etc.“Invisible” parameters?Measurement reliability?
  18. 18. 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(purposely abstract: measurements- resolutions reference/SI decomposition- not one path (e.g. Briggman et al.= problem specific criteria, not method specific – compare & collaborate projects)
  19. 19. Tools for CharacteristicReference RecordingsLarge arrays ofrecording electrodes+ optogeneticselectivityMicroscopic wirelessprobesMolecular “ticker-tape” by DNAamplification
  20. 20. Tractable AGI through System Identification inNeural CircuitryRepresentations and Models Behavior of interest; Signals of interest; Discovering the transfer functionMental Processes and Neural Circuitry: BrainEmulation System identification in neural circuitrySimplification of an Intractable System into aCollection of System Identification Problems Tools for structural decomposition; Data from structure; Parameter tuning among connected systems; Tools for characteristic reference recordingsChallenges Signals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept
  21. 21. Challenges General 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
  22. 22. Care about signalsContributions outsidespiking domain? Other cells? Neuron-neuron effects without spiking? Evidence of sensations retained?Assume: spikes = currencyof sensations Not epiphenomenal! (test?)
  23. 23. Predicting spikes Observe / deduce spike times original system Additional information aids prediction What information do the tools give us?Izhikevich
  24. 24. MRILarge volumesNot parameter tuningBut system validation! Distribution PropagationRequires spatialregistration
  25. 25. 3D reconstructions at 5nm General classification (e.g. pyramidal vs interneuron) Detailed morphology, segmented into compartments E.g. radius – resistance, capacitance Depends on neuron type Measurement reliability, cumulative
  26. 26. Plasticity & MorphologyLearningchangessynapses &connectomeDeformationchangesmorphology3D snapshotcannot capturetemporaldynamics ofmemory
  27. 27. Ticker-Tape Data Many neurons, several tapes per neuron Time stamps + spike / membrane potential samples Recovery of DNA snippets Not combinable with EM Interference with cell mechanisms Spatial registration: Which part of ultrastructure did it come from?
  28. 28. Optical FunctionalCalcium / proteins,fluorescent Large scale / whole brain access?Methods disturbtissueHuge electrodearrays also disturbtissue
  29. 29. Microscopic wireless Power & data volumes compete with continuous sampling When enough sporadic data? Long term dynamics Demands frequent spatial registration EM registration in tissue Ongoing collaboration (MIT, Harvard)
  30. 30. Sufficient DataSpike times, EFPs,membranepotentials – rate,duration?Response shapesufficient?Stimulatecombinations?
  31. 31. Learning from virtual systems NETMORPH Acquire structure data Acquire functional data Test algorithms & iteratively improving constraints Calculate abstract boundary conditions? Netmorph.org
  32. 32. Proof of Concept: Starting SmallTest process in small system C.Elegans (Dalrymple) Retina (Briggman) Hippocampal neuroprosthetic (Berger) Cerebellar neuroprosthetic (Bamford) Memory from piece of neural tissue (Seung)
  33. 33. DiscussionGood gage of Tools = problem 1,problems – proof of turning data intoconcept! model = problem 2SI is not new! True effortMany fields can underway –contribute seeking input from SI experts!
  34. 34. ThanksEd Boyden (MIT)Yael Maguire (MIT)Konrad Kording(NW)Ken Hayworth (JF)Many others in theWBE group! Carboncopies.org 2045.com

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