Epstein Secrets

1 The Hopfield neural net model 256 13.4.2 Knowledge Representation via Cell Assemblies 257 13.5 Neural Foundations of Learning 258 13.5.1 Hebbian Learning 258 13.5.2 Virtual Synapses and Hebbian Learning Between Assemblies 258 13.5.3 Neural Darwinism 259 13.6 Glocal Memory 260 13.6.1 A Semi-Fo

hs of Intelligent Systems 274 14.4.1 SMEPH Principles in CogPrime 276 15 Emergent Networks of Intelligence 279 15.1 Introduction 279 15.2 Small World Networks 280 15.3 Dual Network Structure 281 15.3.1 Hierarchical Networks 281 15.3.2 Associative, Heterarchical Networks 282 15.3.3 Dual Networks

e our points about glocal knowledge representation in neural net type systems without discussing some aspects of learning in these systems. 13.5.1 Hebbian Learning The most common and plausible assumption about learning in the brain is that synaptic connec- tions between neurons are adapted via some variant of

the possible emergence of structures characterizing inter-cognitive reflection. 279 EFTA00624055 280 15 Emergent Networks of Intelligence 15.2 Small World Networks One simple but potentially useful observation about CogPrime Atomspaces is that they are generally going to be small world networks Inuc03], rather

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rks of Intelligence..............00... 002. c eee eee 279 L5el JPOAWEtIOH, «205 seme emees Cees EMNOs LE SMEG MEME SEMS SERS EM E!s dees ema 279 15.2 Small World Networks ........0..000.0 0000 c cette 280 15.3 Dual Network Structure .......0..0.0 000 ete 281 15.3.1 Hierarchical Networks...........0.00.000 00 eee ee 281

ake our points about glocal knowledge representation in neural net type systems without discussing some aspects of learning in these systems. 13.5.1 Hebbian Learning The most common and plausible assumption about learning in the brain is that synaptic connec- tions between neurons are adapted via some variant of

sible emergence of structures characterizing inter-cognitive reflection. 279 HOUSE_OVERSIGHT_013195 280 15 Emergent Networks of Intelligence 15.2 Small World Networks One simple but potentially useful observation about CogPrime Atomspaces is that they are generally going to be small world networks [Buc03], rather