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EE1411
Design of Self-Organizing Learning Design of Self-Organizing Learning Array for Intelligent MachinesArray for Intelligent Machines
Janusz StarzykSchool of Electrical Engineering and Computer Science
Heidi Meeting June 3 2005
Motivation:How a new understanding of the brain will lead to the creation of truly intelligent machines
from J. Hawkins “On Intelligence”
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Abstract thinking and action planning Capacity to learn and memorize useful things Spatio-temporal memories Ability to talk and communicate Intuition and creativity Consciousness Emotions and understanding others Surviving in complex environment and adaptation Perception Motor skills in relation to sensing and anticipation
Elements of IntelligenceElements of Intelligence
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Problems of Classical AIProblems of Classical AI
Lack of robustness and generalization No real-time processing Central processing of information by a
single processor No natural interface to environment No self-organization Need to write software
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Intelligent BehaviorIntelligent Behavior
Emergent from interaction with environment Based on large number of sparsely connected
neurons Asynchronous Self-timed Interact with environment through sensory-
motor system Value driven Adaptive
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Design principles of intelligent systemsDesign principles of intelligent systems
Design principlessynthetic methodologytime perspectivesemergencediversity/complianceframe-of-reference
from Rolf Pfeifer “Understanding of Intelligence”
Agent designcomplete agent principle
cheap design
ecological balance
redundancy principle
parallel, loosely coupled processes
sensory-motor coordination
value principle
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The principle of “cheap design”The principle of “cheap design”
intelligent agents: “cheap” exploitation of ecological
niche economical (but redundant) exploitation of specific
physical properties of interaction with real world
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Principle of “ecological balance”Principle of “ecological balance”
balance / task distribution between morphology neuronal processing (nervous
system) materials environment
balance in complexity given task environment match in complexity of sensory,
motor, and neural system
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The redundancy principleThe redundancy principle
redundancy prerequisite for adaptive behavior
partial overlap of functionality in different subsystems
sensory systems: different physical processes with “information overlap”
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Generation of sensory stimulation Generation of sensory stimulation through interaction with environmentthrough interaction with environment
multiple modalities constraints from
morphology and materials
generation of correlations through physical process
basis for cross-modal associations
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The principle of sensory-motor The principle of sensory-motor coordinationcoordination
self-structuring of sensory data through interaction with environment
physical process —not „computational“
prerequisite for learning
Holk Cruse•no central control•only local neuronal communication•global communication through environment
neuronal connections
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The principle of parallel, loosely The principle of parallel, loosely coupled processescoupled processes
Intelligent behavior emergent from agent-environment interaction
Large number of parallel, loosely coupled processes
Asynchronous Coordinated through agent’s –sensory-motor system–neural system–interaction with environment
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Human Human Brain Brain at Birthat Birth 6 Years Old6 Years Old
14 Years 14 Years OldOld
Neuron Structure and Self-Organizing Principles
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Neuron Structure and Self-Neuron Structure and Self-Organizing Principles (Cont’d)Organizing Principles (Cont’d)
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Broca’sarea
Parsopercularis
Motor cortex Somatosensory cortex
Sensory associativecortex
PrimaryAuditory cortex
Wernicke’sarea
Visual associativecortex
Visualcortex
Brain OrganizationBrain Organization
While we learn its functionscan we emulate its operation?
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V. Mountcastle argues that all regions of the brain perform the same algorithm
SOLAR combines many groups of neurons (minicolumns) in a pseudorandom way
Each microcolumn has the same structure Thus it performs the same computational
algorithm satisfying Mountcastle’s principle
VB Mountcastle (2003). Introduction [to a special issue of Cerebral Cortex on columns]. Cerebral Cortex, 13, 2-4.
Minicolumn Organization and Minicolumn Organization and Self Organizing Learning ArraysSelf Organizing Learning Arrays
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“The basic unit of cortical operation is the minicolumn … It contains of the order of 80-100 neurons except in the primate striate cortex, where the number is more than doubled. The minicolumn measures of the order of 40-50 m in transverse diameter, separated from adjacent minicolumns by vertical, cell-sparse zones … The minicolumn is produced by the iterative division of a small number of progenitor cells in the neuroepithelium.” (Mountcastle, p. 2)
Stain of cortex in planum temporale.
Cortical MinicolumnsCortical Minicolumns
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Groupings of minicolumns seem to form the physiologically observed functional columns. Best known example is orientation columns in V1.
They are significantly bigger than minicolumns, typically around 0.3-0.5 mm and have 4000-8000 neurons
Mountcastle’s summation:
“Cortical columns are formed by the binding together of many minicolumns by common input and short range horizontal connections. … The number of minicolumns per column varies … between 50 and 80. Long range intracortical projections link columns with similar functional properties.” (p. 3)
Groupping of MinicolumnsGroupping of Minicolumns
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Sparse ConnectivitySparse Connectivity The brain is sparsely connected.
(Unlike most neural nets.) A neuron in cortex may have on the order of 100,000 synapses.
There are more than 1010 neurons in the brain. Fractional connectivity is very low: 0.001%.
Implications: Connections are expensive biologically since they take up
space, use energy, and are hard to wire up correctly. Therefore, connections are valuable. The pattern of connection is under tight control. Short local connections are cheaper than long ones.
Our approximation makes extensive use of local connections for computation.
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Introducing Self-Organizing Introducing Self-Organizing Learning Array SOLARLearning Array SOLAR
SOLAR is a regular array of identical processing cells, connected to programmable routing channels. Each cell in the array has ability to self-organize by adapting its functionality in response to information contained in its input signals. Cells choose their input signals from the adjacent routing channels and send their output signals to the routing channels.Processing cells can be structured to implement minicolumns
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Wiring in SOLARWiring in SOLAR
Initial wiring and final wiring selection for credit card approval problem
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Brain Structure with Value System PropertiesBrain Structure with Value System Properties
Interacts with environment through sensors and actuators
Uses distributed processing in sparsely connected neurons organized in minicolumns
Uses spatio-temporal associative learning Uses feedback for input prediction and screening
input information for novelty Develops an internal value system to evaluate its
state in environment using reinforcement learning Plans output actions for each input to maximize the
internal state value in relation to environment Uses redundant structures of sparsely connected
processing elements
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Sensors Actuators
Value System
Anticipated ResponseReinf. Signal
Sensory Inputs
Motor Outputs
ActionPlanning
Understanding ImprovementDetection
Expectation
NoveltyDetection
Inhibition Comparison
Possible Minicolumn Organization Possible Minicolumn Organization
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Learning should be restricted to unexpected situation or reward
Anticipated response should have expected value Novelty detection should also apply to the value
system Need mechanism to improve and compare the value Anticipated response block should learn the response
that improves the value A RL optimization mechanism may be used to learn
the optimum response for a given value system and sensory input
Random perturbation should be applied to the optimum response to explore possible states and learn their the value
New situation will result in new value and WTA will chose the winner
Postulates for Minicolumn OrganizationPostulates for Minicolumn Organization
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Minicolumn Selective ProcessingMinicolumn Selective Processing
Sensory inputs are represented by more and more abstract features in the sensory inputs hierarchy
Possible implementation is to use winner takes all or Hebbian circuits to select the best match
“Sameness principle” of the observed objects to detect and learn feature invariances
Time overlap of feature neuron activation to store temporal sequences
Random wiring may be used to preselect sensory features
Uses feedback for input prediction and screening input information for novelty
Uses redundant structures of sparsely connected processing elements
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Minicolumn OrganizationMinicolumn Organization
Positive Reinforcement
Negative Reinforcement
Sensory Inputs
Motor Outputs
Sensory
Value
Motor
superneuron
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Sensory neurons are primarily responsible for providing information about environment They receive inputs from sensors or other sensory neurons on lower level They interact with motor neurons to represent action and state of
environment They provide an input to reinforcement neurons They help to activate motor neurons
Motor neurons are primarily responsible for activation of motor functions They are activated by reinforcement neurons with the help from sensory
neurons They activate actuators or provide an input to lower level motor neurons They provide an input to sensory neurons
Reinforcement neurons are primarily responsible for building the internal value system They receive inputs from reinforcement learning sensors or other
reinforcement neurons on lower level They receive inputs from sensory neurons They provide an input to motor neurons They help to activate sensory neurons
Minicolumn OrganizationMinicolumn Organization
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Sensory Neurons FunctionsSensory Neurons FunctionsSensory neurons Represent inputs from environment by
Responding to activation from lower level (summation)Selecting most likely scenario (WTA)
Interact with motor functions byResponding to activation from motor outputs (summation)
Anticipate inputs and screen for novelty byCorrelation to sensory inputs from higher levelInhibition of outputs to higher level
Select useful information byCorrelating its outputs with reinforcement neurons
Identify invariances byMaking spatio-temporal associations between neighbor sensory neurons
WTAWTA
WTA