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Toward Tractable AGI: Challenges for System Identification in Neural Circuitry
The document discusses the challenges of system identification in neural circuitry for advancing artificial general intelligence (AGI). It emphasizes the need for simplification and decomposition of complex neural systems into manageable components, tools for parameter tuning, and validation methods. By addressing issues like data reliability and the communication between neuronal circuits, the document outlines a roadmap toward improved brain emulation and practical AGI systems.
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Toward Tractable AGI: Challenges for System Identification in Neural Circuitry
- 1. Toward Tractable AGI:Challenges for System 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. Tractable AGI throughSystem Identification in Neural Circuitry Representations and Models Behavior 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 Challenges Signals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept
- 3. Tractable AGI Challenges in our environment Theoretically sound AGI Practical feasibility Short-cuts Brain-like AGI Legg, Hutter Optimal boundedlength spacetime embedded agent 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. Tractable AGI throughSystem Identification in Neural Circuitry Representations and Models Behavior 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 Challenges Signals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept
- 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. Behavior of Interest Lotsof pieces Systematic modeling Keep it simple Interesting effect Focus, constrain scope/model Neuroscience: Effect = Behavior (e.g. object recognition)
- 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. Discovering the Transfer Function SIin Control Theory: Black/gray box State, input, output Find: Transfer Function Formal methods E.g. Volterra series expansion kernels & history of input
- 9. Tractable AGI throughSystem Identification in Neural Circuitry Representations and Models Behavior 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 Challenges Signals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept
- 10. Mental Processes andNeural Circuitry: 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. Reconstruction vs. Abstraction: Interfaces& Prostheses 100 years of component level neuroscience Individual differences Matter to interfaces Matter to prostheses
- 12. System Identification inNeural 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. Tractable AGI throughSystem Identification in Neural Circuitry Representations and Models Behavior 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 Challenges Signals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept
- 14. Simplification of anIntractable 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 modeling Briggman et al & Bock et al, Nature 2011 Characteristic responses ADP, AHP, AMPA, s/fNMDA
- 15. Whole Brain Emulation:A Roadmap to data acquisition & representation Characterize the parts Break into parts. How can the parts communicate? Platform suiting representation Iterating Improving the Other pillars (See earlier presentations, carboncopies.org. More about roadmap & projects – leading up To Global Future 2045 Congress NYC, June 15-16.)
- 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)
- 17. Data from Structure SIfor compartments Electric circuit analogy 3D shape Conductance, class, etc. “Invisible” parameters? Measurement reliability?
- 18. Parameter Tuning amongConnected 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. Tools for Characteristic ReferenceRecordings Large arrays of recording electrodes + optogenetic selectivity Microscopic wireless probes Molecular “ticker- tape” by DNA amplification
- 20. Tractable AGI throughSystem Identification in Neural Circuitry Representations and Models Behavior 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 Challenges Signals and predicting spikes; Validation, reconstruction and plasticity; Interference during measurement; Data quantities; Proof of concept
- 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. Care about signals Contributionsoutside spiking domain? Other cells? Neuron-neuron effects without spiking? Evidence of sensations retained? Assume: spikes = currency of sensations Not epiphenomenal! (test?)
- 23. Predicting spikes Observe / deduce spike times original system Additional information aids prediction What information do the tools give us? Izhikevich
- 24. MRI Large volumes Not parametertuning But system validation! Distribution Propagation Requires spatial registration
- 25. 3D reconstructions at5nm 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. Plasticity & Morphology Learning changes synapses& connectome Deformation changes morphology 3D snapshot cannot capture temporal dynamics of memory
- 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. Optical Functional Calcium /proteins, fluorescent Large scale / whole brain access? Methods disturb tissue Huge electrode arrays also disturb tissue
- 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. Sufficient Data Spike times,EFPs, membrane potentials – rate, duration? Response shape sufficient? Stimulate combinations?
- 31. Learning from virtualsystems NETMORPH Acquire structure data Acquire functional data Test algorithms & iteratively improving constraints Calculate abstract boundary conditions? Netmorph.org
- 32. 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)
- 33. Discussion Good gage of Tools = problem 1, problems – proof of turning data into concept! model = problem 2 SI is not new! True effort Many fields can underway – contribute seeking input from SI experts!
- 34. Thanks Ed Boyden (MIT) YaelMaguire (MIT) Konrad Kording (NW) Ken Hayworth (JF) Many others in the WBE group! Carboncopies.org 2045.com