Epstein Secrets

er-level behaviors like reasoning or subgoaling. e NOMAD (Neurally Organized Mobile Adaptive Device) automata and its successors [KE06] are based on Edelman’s “Neural Darwinism” model of the brain, and feature large numbers of simulated neurons evolving by natural selection into configurations that carry

oduction .......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 SMEPH Vertices .........000 0000. tte 272 14.3.2 SMEPH Edges ...... 0... ee eee eens 273 14.4 Implicat

works... .......0.000. 0022 c cee ee 268 13.6.4 Neural-Symbolic Glocality in CogPrime ............. 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

epresentation...................... 245 13.1 Introduction ..........000 0002 eens 245 13.2 Localized Knowledge Representation using Weighted, Labeled Hypergraphs .... 246 13.2.1 Weighted, Labeled Hypergraphs............... 0000: c eee eee eee eee 246 13.3 Atoms: Their Types and Weights ............ 0.00. cee

r-level behaviors like reasoning or subgoaling. • NOMAD (Neurally Organized Mobile Adaptive Device) automata and its successors [K EMI are based on Edelman's "Neural Darwinism" model of the brain, and feature large numbers of simulated neurons evolving by natural selection into configurations that carry

the Brain 263 13.6.3 Glocal Hopfield Networks 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 Ed

13 Local, Global and Glocal Knowledge Representation 245 13.1 Introduction 245 13.2 Localized Knowledge Representation using Weighted, Labeled Hypergraphs 246 13.2.1 Weighted, Labeled Hypergraphs 246 13.3 Atoms: Their Types and Weights 247 13.3.1 Some Basic Atom Types 247 13.3.2 Variable At

0,000 neurons each. Once one has committed oneself to looking at such groups, the next step is to ask how these groups are organized, which leads to Edelman's concept of "maps." A "map," in Edelman's terminology, is a connected set of groups with the property that when one of the inter-group connection

to Hebbian learning between assemblies has been pursued by Nobelist Gerald Edelman in his theory of neuronal group selection, or "Neural Darwinism." Edelman won a Nobel Prize for his work in immunology, which, like most modern immunology, was based on C. MacFarlane Burnet's theory of "clonal selection"

algorithms for creating localist knowledge from globalist knowledge are described 13.2 Localized Knowledge Representation using Weighted, Labeled Hypergraphs There are many different mechanisms for representing knowledge in AI systems in an explicit, localized way, most of them descending from various va

50,000 neurons each. Once one has committed oneself to looking at such groups, the next step is to ask how these groups are organized, which leads to Edelman’s concept of “maps.” A “map,” in Edelman’s terminology, is a connected set of groups with the property that when one of the inter-group connections

similar transient assemblies when highly activated. The importance of repeated, slightly varied copies of the same subnetwork has been emphasized by Edelman [Ede93] among other neural theorists. 1 The larger an ensemble is, she suggests, the more vivid it is as a conscious experience; an hypothesis that

n” algorithms for creating localist knowledge from globalist knowledge are described 13.2 Localized Knowledge Representation using Weighted, Labeled Hypergraphs There are many different mechanisms for representing knowledge in AI systems in an explicit, localized way, most of them descending from various var