Epstein Secrets

ning 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-Formal Model of Glocal Memory 262 13.6.2 Glocal Memory in the Brain 263 13.6.3 Glocal Hopfield Networks 268 13.6.4 Neural-Symbolic Glocality in CogPrime 269 14 Repre

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

ficant role in the brain and in intelligent systems more broadly. EFTA00624037 262 13 Local, Global and Glocal Knowledge Representation 13.6.1 A Semi-Formal Model of Glocal Memory To explain the notion of glocal memory more precisely, we will introduce a simple semi-formal model of a system $ that uses a memory to record info

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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ole in the brain and in intelligent systems more broadly. HOUSE_OVERSIGHT_013177 262 13 Local, Global and Glocal Knowledge Representation 13.6.1 A Semi-Formal Model of Glocal Memory To explain the notion of glocal memory more precisely, we will introduce a simple semi-formal model of a system S that uses a memory to record infor

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