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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, symbiosis natural selection society organism behaviour sensory-motor learning organism cell synapse protein cell synapse protein amino acid spikes voltage, Ca direct,V,Ca molecular forces synaptic plasticity ( = STDP) bulk molecular changes molecular changes gene expression, protein recycling 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. Separation of timescales allows INTERACTIONS at one LEVEL to be LEARNING at the LEVEL above. Interactions=fast Learning=slow T.Bell A Multi-Level View of Learning LEVEL UNIT DYNAMICS LEARNING ecology society predation, symbiosis natural selection society organism behaviour sensory-motor learning organism cell synapse protein cell synapse protein amino acid spikes voltage, Ca direct,V,Ca molecular forces synaptic plasticity ( = STDP) bulk molecular changes molecular changes gene expression, protein recycling Increasing Timescale LEARNING at one LEVEL is implemented by DYNAMICS between UNITS at the LEVEL below. Separation of timescales allows DYNAMICS at one LEVEL to be LEARNING at the LEVEL above. Dynamics=fast Learning=slow What idea will fill in the question mark? physiology (of STDP) (STDP=spike timingdependent plasticity) T.Bell physics of selforganisation ? 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 T.Bell Infomax between Levels. (eg: synapses density-estimate spikes) 1 t all neural spikes 2 ICA/Infomax between Layers. (eg: V1 density-estimates Retina) 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 pdf of all synaptic ‘readouts’ V1 synaptic weights x retina • within-level • feedforward • molecular sublevel is ‘implementation’ • social superlevel is ‘reward’ • predicts independent activity • only models outside input pdf of all spike times If we can make this pdf uniform then we have a model constructed from all synaptic and dendritic causality ICA transform minimises statistical dependence between outputs. The bases produced are data-dependent, not fixed as in Fourier or Wavelet transforms. 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 T.Bell