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Arithmetic

VIEWS: 3 PAGES: 34

									   Design of Self-Organizing Learning
   Array for Intelligent Machines

        Janusz Starzyk
        School 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” 1
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        Elements of Intelligence

       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

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   Problems 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 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 systems
             from Rolf Pfeifer “Understanding of Intelligence”

Design principles              Agent design
    synthetic methodology     complete agent principle
    time perspectives         cheap design
    emergence                 ecological balance
    diversity/compliance      redundancy principle
    frame-of-reference        parallel, loosely coupled
                                 processes
                               sensory-motor coordination
                               value principle

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    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”

       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 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
   through 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
    coordination
                              Holk Cruse
   self-structuring of          •no central control
                                 •only local
    sensory data through         neuronal
    interaction with             communication
    environment                  •global
   physical process —           communication
                                 through
    not „computational“          environment
   prerequisite for
    learning                     neuronal
                                 connections


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   The principle of parallel, loosely
   coupled 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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   Neuron Structure and Self-
   Organizing Principles




 Human                       14 Years
 Brain
 at Birth
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                                             12
   Neuron Structure and Self-
   Organizing Principles (Cont’d)




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                                          Motor cortex   Somatosensory cortex



          Pars                                                                  Sensory associative
          opercularis                                                           cortex


                                                                                    Visual associative
                                                                                    cortex




Broca’s
area


                                                                                          Visual
                                                                                          cortex



                        Primary
                        Auditory cortex

                                          Wernicke’s
                                          area                                                     While we learn
                                                                                                   its functions
                                                                                                   can we emulate
                        Brain Organization                                                         its operation?

                                                                                                            14
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   Minicolumn Organization and
   Self Organizing Learning Arrays
   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.
                                                                              15
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   Cortical Minicolumns
   “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.

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  Groupping of Minicolumns
  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)
                                                          17
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  Sparse 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
    Learning 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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   SOLAR Hardware Architecture




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   SOLAR Routing Scheme




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   PCB SOLAR




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  System SOLAR




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   Wiring in SOLAR




   Initial wiring and final wiring selection for credit card
   approval problem


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   SOLAR Classification Results




                                  25
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   Associative SOLAR




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   Associations made in SOLAR




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  Brain 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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         Possible Minicolumn Organization

                                  Value System
      Understanding                              Improvement
                                                   Detection
                                   Action
          Expectation              Planning
  Inhibition                                     Comparison
           Novelty
          Detection


Reinf.                             Anticipated Response         Motor
Signal                                                          Outputs

  Sensors               Sensory                           Actuators
                        Inputs
                                                                  29
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 Postulates for Minicolumn Organization
       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                                     30
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  Minicolumn 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 Organization

                          superneuron
                            Value

Positive
                                                Negative
Reinforcement
                                                Reinforcement
                Sensory
                                        Motor




Sensory
Inputs
                                                      Motor
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                                                      Outputs
      Minicolumn Organization
       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


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    Sensory Neurons Functions
 Sensory neurons
 Represent inputs from environment by
                                                               WTA
    Responding to activation from lower level
    (summation)
    Selecting most likely scenario (WTA)
                                                  WTA          WTA
 Interact with motor functions by
    Responding to activation from motor outputs
    (summation)
 Anticipate inputs and screen for novelty by
    Correlation to sensory inputs from higher level
    Inhibition of outputs to higher level
 Select useful information by
    Correlating its outputs with reinforcement neurons
 Identify invariances by
    Making spatio-temporal associations between
    neighbor sensory neurons
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