Epstein Secrets

re Nodes 254 13.3.7 Links for Special External Data Types 254 13.3.8 Truth Values and Attention Values 255 13.4 Knowledge Representation via Attractor Neural Networks 256 EFTA00623773 xviii Contents 13.4.1 The Hopfield neural net model 256 13.4.2 Knowledge Representation via Cell Assemblies 257 13.5

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

ruth values, allowing logical semantics. EFTA00624031 256 13 Local, Global and Global Knowledge Representation 13.4 Knowledge Representation via Attractor Neural Networks Now we turn to global, implicit knowledge representation — beginning with formal neural net models, briefly discussing the brain, and then turning

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

ues, allowing logical semantics. HOUSE_OVERSIGHT_013171 256 13 Local, Global and Glocal Knowledge Representation 13.4 Knowledge Representation via Attractor Neural Networks Now we turn to global, implicit knowledge representation — beginning with formal neural net models, briefly discussing the brain, and then turning b

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