Cognitive Computing Computational Neuroscience Almaden Institute Jerome Swartz

Click to edit Master title style,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,*,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,( = 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,LEVEL,UNIT,INTERACTIONS,LEARNING,society,organism,behaviour,ecology,society,predation,symbiosis,natural selection,sensory-motor,learning,organism,cell,spikes,synaptic plasticity,cell,protein,molecular forces,gene expression,protein recycling,voltage, Ca,bulk molecular changes,synapse,amino acid,synapse,protein,direct,V,Ca,molecular changes,( = 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,LEVEL,UNIT,DYNAMICS,LEARNING,society,organism,behaviour,ecology,society,predation,symbiosis,natural selection,sensory-motor,learning,organism,cell,spikes,synaptic plasticity,cell,protein,molecular forces,gene expression,protein recycling,voltage, Ca,bulk molecular changes,synapse,amino acid,synapse,protein,direct,V,Ca,molecular changes,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:,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:,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.,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 ComputingComputational Neuroscience,