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 UNIT INTERACTIONS LEARNING society organism behaviour ecology predation, symbiosis natural selection sensory-motor learning cell spikes synaptic plasticity protein molecular forces gene expression, protein recycling voltage, Ca bulk molecular changes synapse amino acid direct,V,Ca molecular changes ( = STDP) Increasing Timescale 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. Interactions=fast Learning=slow Separation of timescales allows INTERACTIONS at one LEVEL to be LEARNING at the LEVEL above.

A Multi-Level View of Learning T.Bell LEVEL UNIT DYNAMICS LEARNING society organism behaviour ecology predation, symbiosis natural selection sensory-motor learning cell spikes synaptic plasticity protein molecular forces gene expression, protein recycling voltage, Ca bulk molecular changes synapse amino acid direct,V,Ca molecular changes ( = STDP) Increasing Timescale LEARNING at one LEVEL is implemented by DYNAMICS between UNITS at the LEVEL below. Dynamics=fast Learning=slow Separation of timescales allows DYNAMICS at one LEVEL to be LEARNING at the LEVEL above.

? = the Levels Hypothesis: Learning in the brain is: What idea will fill in the question mark? T.Bell physiology (of STDP) physics of self-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 cell 1 brain network of protein complexes (e.g., synapses) network of macromolecules Networks within networks

Infomax between Levels. (eg: synapses density-estimate spikes) 1 T.Bell Infomax between Levels. (eg: synapses density-estimate spikes) 1 ICA/Infomax between Layers. (eg: V1 density-estimates Retina) 2 t all neural spikes retina V1 synaptic weights x y synapses, dendrites y all synaptic readout between-level includes all feedback molecular net models/creates social net is boundary condition permits arbitrary activity dependencies models input and intrinsic together within-level feedforward molecular sublevel is ‘implementation’ social superlevel is ‘reward’ predicts independent activity only models outside input pdf of all spike times pdf of all synaptic ‘readouts’ ICA transform minimises statistical dependence between outputs. The bases produced are data-dependent, not fixed as in Fourier or Wavelet transforms. If we can make this pdf uniform then we have a model constructed from all synaptic and dendritic causality

The Infomax principle/ICA algorithms 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