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

EPH Vertices 272 14.3.2 SMEPH Edges 273 14.4 Implications of Patternist Philosophy for Derived Hypergraphs 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

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

hen according to the patternist philosophy evolution of its derived hypergraph can't necessarily be expected to follow the above principles. 14.4.1 SMEPH Principles in CogPrime We now more explicitly elaborate the application of these ideas in the CogPrime context. As noted above, in addition to explicit knowle

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rnist Philosophy for Derived Hypergraphs of Intelligent SYSUCIIS ss cm ems FERS HEME HEME OEE OS CREME CR EMS CR EMS ORES OR EME OR EE Ome 274 14.4.1 SMEPH Principles in CogPrime ............. 000... c eee eee 276 15 Emergent Networks of Intelligence..............00... 002. c eee eee 279 L5el JPOAWEtIOH, «205 seme

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

then according to the patternist philosophy evolution of its derived hypergraph can’t necessarily be expected to follow the above principles. 14.4.1 SMEPH Principles in CogPrime We now more explicitly elaborate the application of these ideas in the CogPrime context. As noted above, in addition to explicit knowled