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Reverse Engineering the Brain James Albus Senior Fellow Krasnow Institute for Advanced Studies George Mason University [email protected] Krasnow Institute for Advanced Studies -- George Mason University Outline What does reverse engineering mean? Some neural computational mechanisms An example from visual perception What is a path to success? Krasnow Institute for Advanced Studies -- George Mason University Reverse Engineering the Brain Building computational machines that are functionally equivalent to the brain in their ability to perceive, think, decide, and act in a purposeful way to achieve goals in complex, uncertain, dynamic, and possibly hostile environments, despite unexpected events and unanticipated obstacles, while guided by internal values and rules of conduct. Functional equivalence Producing the same input/output behavior Krasnow Institute for Advanced Studies -- George Mason University Reverse Engineering the Brain Will require a deep understanding of how the brain works and what the brain does How is information represented in the brain? How is computation performed? What are the functional operations? What are the knowledge data structures? How are messages encoded? How are images processed? How are relationships established and broken? How are signals transformed into into symbols? How does the brain generate the incredibly complex colorful, dynamic internal representation that we consciously perceive as external reality? Krasnow Institute for Advanced Studies -- George Mason University Engineering Requires a Scientific Model Resolution of the model? • overall system level (central nervous system) • arrays of macro-computational units (e.g., cortical regions) • macro-computational units (e.g., cortical hypercolumns & loops) • micro-computational units (e.g., cortical microcolumns & loops) • neural clusters (e.g., spinal and midbrain sensory-motor nuclei) • neurons (elemental computational units) – input/output functions • synapses (electronic gates, memory elements) – synaptic phenomena • membrane mechanics (ion channel activity) – molecular phenomena Krasnow Institute for Advanced Studies -- George Mason University Computational Mechanisms Synapse is an electronic gate Neuron is an atomic computational element Neural Cluster is a functional element – capable of: arithmetic or logical operations, correlation, coordinate transformation, finite-state automata, grammar, direct and indirect addressing Cortical Computational Unit is a collection of functional elements – capable of: focus of attention, segmentation and grouping, calculation of group attributes and state, classification, and establishing relationships Krasnow Institute for Advanced Studies -- George Mason University A Typical Neural Cluster S(t) P(t + Dt) = H(S(t)) e.g. in the cerebellum (Marr 1969, Albus 1971), memory recall, arithmetic or logical functions, IF/THEN rules, goal-seeking reactive control, inverse kinematics, direct & indirect addressing Krasnow Institute for Advanced Studies -- George Mason University Neural Clusters in Spinal Cord Krasnow Institute for Advanced Studies -- George Mason University A Neural Cluster + Feedback S(t) P(t + Dt) = H(S(t)) differential and integral functions, dynamic models, time and frequency analysis, phase-lock loops Krasnow Institute for Advanced Studies -- George Mason University A Neural Finite State Automaton S(t) State P(t + Dt) = H(S(t)) Next state Markov processes, scripts, plans, behaviors, grammars Krasnow Institute for Advanced Studies -- George Mason University Cortical Columns Microcolumns 100 – 250 neurons 30 – 50 m diameter, 3000 m long e.g., detect patterns, compute pattern attributes Hypercolumns (a.k.a. columns) 100+ microcolumns in a bundle 500 m in diameter, 3000 m long Basis of Cortical Computational Unit (CCU) There are about 106 hypercolumns in human cortex Krasnow Institute for Advanced Studies -- George Mason University Communication Axon is an active fiber connecting one neuron to others (i.e., publish-subscribe network, bandwidth ~ 500 Hz) Two kinds of axons: • Drivers – Preserve topology and local sign Convey data (attributes) e.g., color, intensity, shape, size, orientation, motion • Modulators – Don’t preserve topology or local sign Convey context (addresses, pointers)* e.g., select & modify algorithms, establish relationships *my hypothesis Sherman & Guillery 2006 Krasnow Institute for Advanced Studies -- George Mason University Example from Vision Left field of view Right side of brain Retina Lateral Geniculate V1 Krasnow Institute for Advanced Studies -- George Mason University Representation of Pixels from the Retina Krasnow Institute for Advanced Studies -- George Mason University Similar Representation of Pixels from the Skin Krasnow Institute for Advanced Studies -- George Mason University Architecture of Vision Hypercolumn Modulator Input Modulator Output to other cortical regions Input from lgn Cortical Columns in V1 + Lateral Geniculate in Thalamus Driver Output to Higher Level & superior colliculus Modulator Output Back to lgn Microcolumn Diffuse Fibers (Modulators) Receptive field Krasnow Institute for Advanced Studies -- George Mason University Cortical Hypercolumn + Thalamic Nucleus Cortical Computational Unit (CCU) Drivers CCU Outputs (data) Modulators (addresses) Krasnow Institute for Advanced Studies -- George Mason University Cortical Hypercolumn + Thalamic loop Cortical Computational Unit (CCU) drivers = attribute vectors modulators = address pointers windowing, segmentation, grouping, computing group attributes & state, filtering, classification, setting and breaking relationships Krasnow Institute for Advanced Studies -- George Mason University Cortico-Thalamic Loop drivers = attribute vectors modulators = address pointers thalamic nucleus t 1 cortical 2 hypercolumn 3 4 5 6 A Cortical Computational Unit (CCU) windowing segmentation & grouping compute group attributes recursive filtering classification Krasnow Institute for Advanced Studies -- George Mason University CorticoThalamic Loop Hierarchy Krasnow Institute for Advanced Studies -- George Mason University Receptive Field Hierarchy Defined by driver neurons flowing up the processing hierarchy Krasnow Institute for Advanced Studies -- George Mason University Segmentation & Grouping Process Each level detects patterns within its receptive field in the level below Krasnow Institute for Advanced Studies -- George Mason University Grouping Pointers Krasnow Institute for Advanced Studies -- George Mason University Grouping Hierarchy Defined by segmentation and grouping processes Pointers link symbols to pixels & vice versa Provide symbol grounding Pointers reset every saccade ~ 150 ms Krasnow Institute for Advanced Studies -- George Mason University Cortex is Remarkably Uniform In posterior cortex, drivers flow up CCUs link signals to symbols & vice versa -- from pixels to objects and situations (in space) -- from frequencies to events and episodes (in time) In frontal cortex, drivers flow down CCUs select goals, set priorities, make plans, and control behavior with intent to achieve goals despite uncertainties Krasnow Institute for Advanced Studies -- George Mason University Behavior Generation In the frontal cortex, hierarchical arrays of CCUs are capable of: decision-making, planning, coordinating, & controlling millions of muscle fibers in effective goal-directed adaptive behavior Krasnow Institute for Advanced Studies -- George Mason University Desired Goal & Contemplated Plan CorticoThalamic Loop in Frontal Cortex Predicted Results of Plan Timing & Sync Select Best Plan Spatial Model of External World Command to Execute Plan This is a Planning Loop Dynamic Model of Own Body From Kandel & Schwartz Krasnow Institute for Advanced Studies -- George Mason University 2001 What is the path to success for reverse engineering the brain? Pick the right level of resolution There are 1011 neurons and 1015 synapses in the brain Real-time modeling at this resolution is well beyond current technology Real-time modeling ::= 20 cycles per second Krasnow Institute for Advanced Studies -- George Mason University What is the path to success? Pick the right level of resolution There are 106 CCUs in the human cortex Real-time modeling at this resolution seems within current technology There are ~ 104 neurons in a CCU Real-time modeling at this resolution seems within current technology Krasnow Institute for Advanced Studies -- George Mason University Computational Estimates State of art supercomputer 3 x 1014 fops Allocating this to 106 CCUs running at 20 Hz yields 1.5 x 107 fops per CCU per cycle Estimated communication load of about 3 x 105 bytes per second for each CCU, or 3 x 1011 bps for full brain model This appears to be within the state of the art Krasnow Institute for Advanced Studies -- George Mason University Summary & Conclusions • Reverse engineering the brain requires selecting the right level of resolution, e.g. functional modules and connecting circuitry • Cortical Computational Unit (CCU) is a fundamental functional module in cortex • Each CCU consists of -- a frame with attributes and pointers -- computational processes to maintain it • Real-time modeling at level of functional modules appears feasible with current supercomputers -- maybe with PC computers in 20 years Krasnow Institute for Advanced Studies -- George Mason University Questions? Krasnow Institute for Advanced Studies -- George Mason University