Transcript Slide 1

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
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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
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Neural Clusters in Spinal Cord
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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
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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
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Representation of Pixels
from the Retina
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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
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Cortical Hypercolumn + Thalamic Nucleus
Cortical Computational Unit (CCU)
Drivers
CCU
Outputs
(data)
Modulators
(addresses)
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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
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CorticoThalamic
Loop
Hierarchy
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Receptive Field Hierarchy
Defined by driver neurons flowing up the processing hierarchy
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Segmentation
& Grouping
Process
Each level detects
patterns within its
receptive field
in the level below
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Grouping Pointers
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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
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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
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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?
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