Epstein Secrets

ng 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-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 Neu

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

evolutionary neurodynamics and the more abstract sort of evolution that patternist philosophy posits to occur in the realm of mind-patterns. 13.6 Glocal Memory A glocal memory is one that transcends the global local dichotomy and incorporates both aspects in a tightly interconnected way. Here we make the g

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

and Hebbian Learning Between Assemblies .......... 258 35:38 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

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

en evolutionary neurodynamics and the more abstract sort of evolution that patternist philosophy posits to occur in the realm of mind-patterns. 13.6 Glocal Memory A glocal memory is one that transcends the global/local dichotomy and incorporates both aspects in a tightly interconnected way. Here we make the gl

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