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

268 13.6.4 Neural-Symbolic Glocality in CogPrime 269 14 Representing Implicit Knowledge via Hypergraphs 271 14.1 Introduction 271 14.2 Key Vertex and Edge Types 271 14.3 Derived Hypergraphs 272 14.3.1 SMEPH Vertices 272 14.3.2 SMEPH Edges 273 14.4 Implications of Patternist Philoso

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

ergent" hypergraphs of the sort described here using the acronym SMEPH, which stands for Self-Modifying, Evolving Probabilistic Hy- pergraphs. 14.2 Key Vertex and Edge Types We begin by introducing a particular collection of Vertex and Edge types, to be used in modeling the internal structures of intellig

8 Netital DArwitiiSitt +2; cases cose: cs s@5m5 $@EME DOES SERS EOE RS Emtes CO: 259 13.6 Glocal Memory .......00 0.0 cc eee t ene e eae 260 13.6.1 A Semi-Formal Model of Glocal Memory .............0...0 00 e eevee 262 13.6.2 Glocal Memory in the Brain ............ 000... eee 263 13.6.3 Glocal Hopfield Networks... .......0.000. 0022

0000.0 269 14 Representing Implicit Knowledge via Hypergraphs ....................... 271 14.1 Introduction .......00..000 000 tte teen ee 271 14.2: Key Vertex atid Edge Typé$ casas cuine cs ce ees ee ses ems es emo ws Meee MERE OE 271 14.3 Derived Hypergraphs .......... 0.000. c cece tenet eae 272 14.3.1 SM

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

mergent" hypergraphs of the sort described here using the acronym SMEPH, which stands for Self-Modifying, Evolving Probabilistic Hy- pergraphs. 14.2 Key Vertex and Edge Types We begin by introducing a particular collection of Vertex and Edge types, to be used in modeling the internal structures of intellige