Large-Scale Brain Modeling by pengxiuhui

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									  Cognitive Computing….
Computational Neuroscience
        Jerome Swartz
      The Swartz Foundation
          May 10, 2006
     Large Scale Brain Modeling
• Science IS modeling
• Models have power
  – To explain
  – To predict
  – To simulate
  – To augment




                  Why model the brain?
         Brains are not computers …
• But they are supported by the same physics
   Energy conservation
   Entropy increase
   Least action
   Time direction

• Brains are supported by the same logic,
      but implemented differently…
  – Low speed; parallel processing; no symbolic software layer;
    fundamentally adaptive / interactive; organic vs. inorganic
  Brain research must be multi-level
• Scientific collaboration is needed
  – Across spatial scales
  – Across time scales
  – Across measurement techniques
• Current field borders should not remain
  boundaries… Curtail Scale Chauvinism!
 …both scientifically and mathematically
• To understand, both theoretically and practically,
  how brains support behavior and experience
• To model brain / behavior dynamics as Active
  requires
  – Better behavioral measures and modeling
  – Better brain dynamic imaging / analysis
  – Better joint brain / behavior analysis
      … the next research frontier
• Brains are active and multi-scale / multi-level
• The dominant multi-level model: Computers
   … with their physical / logical computer hierarchy
   – the OSI stack
   – physical / implementation levels
   – logical / instruction levels
A Multi-Level View of Learning

 LEVEL         UNIT        INTERACTIONS             LEARNING

 ecology      society          predation,         natural selection
                               symbiosis
  society    organism                              sensory-motor
                               behaviour
                                                   learning

 organism        cell            spikes          synaptic plasticity
                                                                         (      = STDP)
  cell         synapse      voltage, Ca        bulk molecular changes

 synapse       protein       direct,V,Ca       molecular changes
                                                                             Increasing
 protein     amino acid     molecular forces     gene expression,            Timescale
                                                 protein recycling

LEARNING at a LEVEL is CHANGE IN INTERACTIONS between its UNITS,
implemented by INTERACTIONS at the LEVEL beneath, and by extension
resulting in CHANGE IN LEARNING at the LEVEL above.
Separation of timescales allows INTERACTIONS at one LEVEL              Interactions=fast
to be LEARNING at the LEVEL above.                                     Learning=slow
               A Multi-Level View of Learning                                    T.Bell



     LEVEL         UNIT          DYNAMICS               LEARNING

    ecology       society         predation,          natural selection
                                  symbiosis
     society     organism                              sensory-motor
                                  behaviour
                                                       learning

    organism         cell           spikes           synaptic plasticity
                                                                           (       = STDP)
      cell         synapse     voltage, Ca         bulk molecular changes

    synapse       protein       direct,V,Ca        molecular changes
                                                                               Increasing
    protein      amino acid     molecular forces     gene expression,          Timescale
                                                     protein recycling


               LEARNING at one LEVEL is implemented by
               DYNAMICS between UNITS at the LEVEL below.

Separation of timescales allows DYNAMICS at one LEVEL                Dynamics=fast
to be LEARNING at the LEVEL above.                                   Learning=slow
                                                              T.Bell
     What idea will fill in the question mark?
               physiology             physics of self-
               (of STDP)              organisation
  (STDP=spike timing-
  dependent plasticity)         ?
                          probabilistic
                          machine learning

? = the Levels Hypothesis:              Learning in the brain is:

-unsupervised probability density estimation across scales

- the smaller (molecular) models the larger (spikes)….
  suggested by STDP physiology, where information flow
  from neurons to synapses is inter-level….
Multi-level modeling:
    network of neurons


                      network of 2 brains




       1 brain                               1 cell


network of protein complexes
(e.g., synapses)
                                  network of macromolecules
              Networks within networks
                                                                                                  T.Bell
 1        Infomax between Levels.                                        ICA/Infomax between Layers.
                                                                   2
          (eg: synapses density-estimate spikes)                         (eg: V1 density-estimates Retina)
                       t      all neural spikes
                                                                           y         V1
                                            synapses,
                                            dendrites                                  synaptic
                                                                                       weights
     y                      all synaptic readout                           x       retina

         • between-level
                                                                   • within-level
         • includes all feedback
                                                                   • feedforward
         • molecular net models/creates
                                                                   • molecular sublevel is ‘implementation’
         • social net is boundary condition
                                                                   • social superlevel is ‘reward’
         • permits arbitrary activity dependencies
                                                                   • predicts independent activity
         • models input and intrinsic together
                                                                   • only models outside input
                                          pdf of all spike times
         pdf of all synaptic ‘readouts’
                                                                       ICA transform minimises statistical
              If we can                                                dependence between outputs. The
              make this                                                bases produced are data-dependent,
              pdf uniform
                                                                       not fixed as in Fourier or Wavelet
                                                                       transforms.
then we have a model
constructed from all synaptic and dendritic causality
                                                              T.Bell
          The Infomax principle/ICA algorithms




Many applications (6 international ICA workshops)…
• audio separation in real acoustic environments (as above)
• biomedical data-mining -- EEG,fMRI,
• image coding


         Cognitive Computing…Computational Neuroscience

								
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