Cognitive Computing…. Computational Neuroscience

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. LEVEL UNIT INTERACTIONS LEARNING predation, symbiosis ecology society natural selection sensory-motor learning society organism behaviour organism cell spikes synaptic plasticity cell synapse voltage, Ca bulk molecular changes synapse molecular changes protein direct,V,Ca gene expression, protein recycling protein amino acid molecular forces A Multi-Level View of Learning ( = 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.

9. LEVEL UNIT DYNAMICS LEARNING predation, symbiosis society natural selection ecology sensory-motor learning organism society behaviour synaptic plasticity organism cell spikes cell synapse voltage, Ca bulk molecular changes synapse molecular changes protein direct,V,Ca gene expression, protein recycling protein amino acid molecular forces A Multi-Level View of Learning T.Bell ( = 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.

10. 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….

11. 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

12. y V1 synaptic weights retina x T.Bell Infomax between Levels. (eg: synapses density-estimate spikes) 1 ICA/Infomax between Layers. (eg: V1 density-estimates Retina) 2 all neural spikes t 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

13. 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