1. Cognitive Computing…. Computational Neuroscience Jerome Swartz The Swartz Foundation May 10, 2006

2. Large Scale Brain Modeling Science IS modeling Models have power –To explain –To predict –To simulate –To augment Why model the brain?

3. 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

4. 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!

5. …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

6. … 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

7. ( = 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 extension resulting in CHANGE IN LEARNING at the LEVEL above. Increasing Timescale Separation of timescales allows INTERACTIONS at one LEVEL to be LEARNING at the LEVEL above. Interactions=fast Learning=slow LEVELUNITINTERACTIONSLEARNING societyorganism behaviour ecologysociety predation, symbiosis natural selection sensory-motor learning organismcellspikessynaptic plasticity cell proteinmolecular forces gene expression, protein recycling voltage, Ca bulk molecular changes synapse amino acid synapse proteindirect,V,Ca molecular changes

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

10. 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

11. network of 2 brains network of neurons network of macromolecules network of protein complexes (e.g., synapses) Networks within networks 1 cell 1 brain Multi-level modeling:

12. 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 times pdf of all synaptic ‘readouts’ 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. T.Bell

13. 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