Cognitive Computing Computational Neuroscience Almaden Institute Jerome Swartz

1、Cognitive Computing.Computational NeuroscienceJerome SwartzThe Swartz FoundationMay 10, 2006Large Scale Brain ModelingScience IS modelingModels have powerTo explainTo predictTo simulateTo augmentWhy model the brain?Brains are not computers But they are supported by the same physics Energy conservati

2、on Entropy increase Least action Time directionBrains are supported by the same logic, but implemented differentlyLow speed; parallel processing; no symbolic software layer; fundamentally adaptive / interactive; organic vs. inorganic Brain research must be multi-levelScientific collaboration is need

3、edAcross spatial scalesAcross time scalesAcross measurement techniquesCurrent field borders should not remain boundaries Curtail Scale Chauvinism!both scientifically and mathematicallyTo understand, both theoretically and practically, how brains support behavior and experienceTo model brain / behavi

4、or dynamics as Active requiresBetter behavioral measures and modelingBetter brain dynamic imaging / analysisBetter joint brain / behavior analysis the next research frontierBrains are active and multi-scale / multi-levelThe dominant multi-level model: Computers with their physical / logical computer

5、 hierarchy the OSI stackphysical / implementation levelslogical / instruction levels( = STDP)A Multi-Level View of LearningLEARNING 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 ab

6、ove.IncreasingTimescaleSeparation of timescales allows INTERACTIONS at one LEVEL to be LEARNING at the LEVEL above.Interactions=fastLearning=slowLEVELUNITINTERACTIONSLEARNINGsocietyorganismbehaviourecologysocietypredation, symbiosisnatural selectionsensory-motorlearningorganismcellspikessynaptic pla

7、sticitycellproteinmolecular forcesgene expression,protein recyclingvoltage, Cabulk molecular changessynapseamino acidsynapseproteindirect,V,Ca molecular changes( = STDP)A Multi-Level View of LearningLEARNING at one LEVEL is implemented byDYNAMICS between UNITS at the LEVEL below.IncreasingTimescaleS

8、eparation of timescales allows DYNAMICS at one LEVEL to be LEARNING at the LEVEL above.Dynamics=fastLearning=slowLEVELUNITDYNAMICSLEARNINGsocietyorganismbehaviourecologysocietypredation, symbiosisnatural selectionsensory-motorlearningorganismcellspikessynaptic plasticitycellproteinmolecular forcesge

9、ne expression,protein recyclingvoltage, Cabulk molecular changessynapseamino acidsynapseproteindirect,V,Ca molecular changesWhat idea will fill in the question mark?physiology (of STDP)physics of self-organisationprobabilistic machine learning?(STDP=spike timing-dependent plasticity) -unsupervised p

10、robability 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: network of 2 brainsnetwork of neuronsnetwork of macromolec

11、ulesnetwork of protein complexes(e.g., synapses)Networks within networks1 cell1 brainMulti-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

12、outside inputretinaV1synaptic weightsxyInfomax 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 togetherall neural spikesall syna

13、ptic readoutsynapses,dendritestypdf of all spike timespdf of all synaptic readoutsIf we canmake thispdf uniformthen we have a model constructed from all synaptic and dendritic causalityICA transform minimises statisticaldependence between outputs. The bases produced are data-dependent,not fixed as in Fourier or Wavelettransforms.The Infomax principle/ICA al