cognitive computing…. computational neuroscience
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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 …. - PowerPoint PPT PresentationTRANSCRIPT
Cognitive Computing….Computational Neuroscience
Jerome SwartzThe 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
( = STDP)
A Multi-Level View of Learning
LEARNING at a LEVEL is CHANGE IN INTERACTIONS between its UNITS,implemented by INTERACTIONS at the LEVEL beneath, and by extensionresulting in CHANGE IN LEARNING at the LEVEL above.
IncreasingTimescale
Separation of timescales allows INTERACTIONS at one LEVEL to be LEARNING at the LEVEL above.
Interactions=fastLearning=slow
LEVEL UNIT INTERACTIONS LEARNING
society organism behaviour
ecology society predation, symbiosis
natural selection
sensory-motorlearning
organism cell spikes synaptic plasticity
cell
protein molecular forces gene expression,protein recycling
voltage, Ca bulk molecular changessynapse
amino acid
synapse protein direct,V,Ca molecular changes
( = STDP)
A Multi-Level View of Learning
LEARNING at one LEVEL is implemented byDYNAMICS between UNITS at the LEVEL below.
IncreasingTimescale
Separation of timescales allows DYNAMICS at one LEVEL to be LEARNING at the LEVEL above.
Dynamics=fastLearning=slow
LEVEL UNIT DYNAMICS LEARNING
society organism behaviour
ecology society predation, symbiosis
natural selection
sensory-motorlearning
organism cell spikes synaptic plasticity
cell
protein molecular forces gene expression,protein recycling
voltage, Ca bulk molecular changessynapse
amino acid
synapse protein direct,V,Ca molecular changes
T.Bell
What idea will fill in the question mark?
physiology (of STDP)
physics of self-organisation
probabilistic machine learning
?(STDP=spike timing-dependent plasticity)
-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….
? = the Levels Hypothesis: Learning in the brain is:
T.Bell
network of 2 brains
network of neurons
network of macromolecules
network of protein complexes(e.g., synapses)
Networks within networks
1 cell1 brain
Multi-level modeling:
ICA/Infomax between Layers.(eg: V1 density-estimates Retina)
2
• within-level• feedforward• molecular sublevel is ‘implementation’• social superlevel is ‘reward’• predicts independent activity• only models outside input
retina
V1
synaptic weights
x
y
Infomax between Levels.(eg: synapses density-estimate spikes)
1
• between-level• includes all feedback• molecular net models/creates• social net is boundary condition• permits arbitrary activity dependencies• models input and intrinsic together
all neural spikes
all synaptic readout
synapses,dendrites
t
y
pdf of all spike timespdf of all synaptic ‘readouts’
If we canmake thispdf uniform
then we have a model constructed from all synaptic and dendritic causality
ICA transform minimises statisticaldependence between outputs. The bases produced are data-dependent,not fixed as in Fourier or Wavelettransforms.
T.Bell
The Infomax principle/ICA algorithms T.Bell
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