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Krasnow Institute for Advanced Studies -- George Mason University
James Albus
Senior FellowKrasnow Institute for Advanced Studies
George Mason University
Reverse Engineeringthe Brain
Krasnow Institute for Advanced Studies -- George Mason University
Outline
What does reverse engineering mean?
An example from visual perception
What is a path to success?
Some neural computational mechanisms
Krasnow Institute for Advanced Studies -- George Mason University
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.
Reverse Engineering the Brain
Functional equivalence
Producing the same input/output behavior
Krasnow Institute for Advanced Studies -- George Mason University
Will require a deep understanding of how thebrain works and what the brain does
How does the brain generate the incredibly complex colorful, dynamic internal representation that
we consciously perceive as external reality?
Reverse Engineering the Brain
How are signals transformed into into symbols?
How are images processed?
How are messages encoded?
How are relationships established and broken?
What are the knowledge data structures?
What are the functional operations?
How is information represented in the brain?
How is computation performed?
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
Cortical Computational Unit is a collection offunctional elements – capable of:
focus of attention, segmentation and grouping,calculation of group attributes and state,classification, and establishing relationships
Neural Cluster is a functional element – capable of:arithmetic or logical operations, correlation, coordinate transformation, finite-state automata, grammar, direct and indirect addressing
Krasnow Institute for Advanced Studies -- George Mason University
A Typical Neural Cluster
e.g. in the cerebellum (Marr 1969, Albus 1971),
S(t) P(t + t) = H(S(t))
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
differential and integral functions, dynamic models,time and frequency analysis, phase-lock loops
S(t) P(t + t) = H(S(t))
A Neural Cluster + Feedback
Krasnow Institute for Advanced Studies -- George Mason University
A Neural Finite State Automaton
Markov processes, scripts, plans, behaviors, grammars
S(t) P(t + t) = H(S(t))
State Next state
Krasnow Institute for Advanced Studies -- George Mason University
Cortical Columns
Microcolumns 100 – 250 neurons 30 – 50 diameter, 3000 longe.g., detect patterns, compute pattern attributes
Hypercolumns (a.k.a. columns)100+ microcolumns in a bundle500 in diameter, 3000 longBasis 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
Sherman & Guillery 2006*my hypothesis
Krasnow Institute for Advanced Studies -- George Mason University
Example fromVision
Retina
Lateral Geniculate
V1
Leftfieldof view
Rightside ofbrain
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
CorticalColumns
in V1 +
LateralGeniculate
in Thalamus
Hypercolumn
Modulator Input
Modulator Output toother cortical regions
Input from lgn
Driver Output to Higher Level & superior colliculus
Modulator OutputBack to lgn
Microcolumn
Receptivefield
DiffuseFibers(Modulators)
Krasnow Institute for Advanced Studies -- George Mason University
Cortical Hypercolumn + Thalamic NucleusCortical Computational Unit (CCU)
Drivers(data)
Modulators(addresses)
CCUOutputs
Krasnow Institute for Advanced Studies -- George Mason University
Cortical Hypercolumn + Thalamic loop
windowing, segmentation, grouping, computing group attributes &state, filtering, classification, setting and breaking relationships
CorticalComputational
Unit(CCU)
drivers = attribute vectors
modulators = address pointers
Krasnow Institute for Advanced Studies -- George Mason University
drivers = attribute vectors
Cortico-Thalamic Loop
123456
t
modulators = address pointers
windowingsegmentation & groupingcompute group attributes
recursive filteringclassification
cortical hypercolumnthalamic
nucleus
A CorticalComputational
Unit(CCU)
Krasnow Institute for Advanced Studies -- George Mason University
Cortico-Thalamic
LoopHierarchy
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 segmentationand grouping processes
Pointers link symbols to pixels& vice versa
Provide symbolgrounding
Pointers reset everysaccade ~ 150 ms
Krasnow Institute for Advanced Studies -- George Mason University
Cortex is Remarkably Uniform
In frontal cortex, drivers flow downCCUs select goals, set priorities,make plans, and control behavior with intent to achieve goals despite uncertainties
In posterior cortex, drivers flow upCCUs link signals to symbols & vice versa-- from pixels to objects and situations (in space)-- from frequencies to events and episodes (in time)
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
Cortico-Thalamic
Loop inFrontalCortex
This is aPlanning Loop
SpatialModelof ExternalWorld
PredictedResults
ofPlan
Dynamic Model of Own Body
Desired Goal & Contemplated Plan
Command to Execute Plan
From Kandel & Schwartz2001
Timing& Sync
SelectBestPlan
Krasnow Institute for Advanced Studies -- George Mason University
What is the path to success forreverse engineering the brain?
Pick the right level of resolution
There are 1011 neurons and 1015 synapsesin the brain
Real-time modeling at this resolutionis 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 resolutionseems 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
State of art supercomputer 3 x 1014 fops
Allocating this to 106 CCUs running at 20 Hzyields 1.5 x 107 fops per CCU per cycle
Estimated communication load of about3 x 105 bytes per second for each CCU, or3 x 1011 bps for full brain model
This appears to be within the state of the art
Computational Estimates
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
• Each CCU consists of -- a frame with attributes and pointers
-- computational processes to maintain it
• Cortical Computational Unit (CCU) is a fundamental functional module in cortex
• 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?