Epstein Secrets

enough, in the sense that they lack sufficiently rich and nuanced interactions EFTA00623805 30 2 Wh

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

zing. Objective detachment from phenomenal self Rich variety of learned mental representations and op

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

enough, in the sense that they lack sufficiently rich and nuanced interactions HOUSE_OVERSIGHT_012945

..........0 002.00 eee eee eee 254 13.3.8 Truth Values and Attention Values ........... 0.00.00 eee eee eee eee 255 13.4 Knowledge Representation via Attractor Neural Networks ................... 256 HOUSE_OVERSIGHT_012913 xviii Contents 13.4.1 The Hopfield neural net model ..............0..0 022 e eee 256 13.4.2 Knowled

Objective Formal detachment from phenomenal self Rich variety of learned mental representations and Con

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