Epstein Secrets

1 The Hopfield neural net model 256 13.4.2 Knowledge Representation via Cell Assemblies 257 13.5 Neural Foundations of Learning 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-Fo

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 Representing Implicit Knowledge via Hypergraphs 271 14.1 Introduction 271 14.2

e our points about glocal knowledge representation in neural net type systems without discussing some aspects of learning in these systems. 13.5.1 Hebbian Learning The most common and plausible assumption about learning in the brain is that synaptic connec- tions between neurons are adapted via some variant of

of glocality in human memory will doubtless improve tremendously. EFTA00624043 268 13 Local, Global and Glace! Knowledge Representation 13.6.3 Glocal Hopfield Networks The ideas in the previous section suggest that, if one wishes to construct an AGI, it is worth seriously considering using a memory with some sort

3.4.2 Knowledge Representation via Cell Assemblies .................2.05- 257 13.5 Neural Foundations of Learning ..........0 0.000 ee eee 258 13.5.1 Hebbian Learning............ 0.0.00 eee ete eee 258 13.5.2 Virtual Synapses and Hebbian Learning Between Assemblies .......... 258 35:38 Netital DArwitiiSitt +2; case

60 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 c cee ee 268 13.6.4 Neural-Symbolic Glocality in CogPrime ............. 0000.0 269 14 Representing Implicit Knowledge via Hyper

ake our points about glocal knowledge representation in neural net type systems without discussing some aspects of learning in these systems. 13.5.1 Hebbian Learning The most common and plausible assumption about learning in the brain is that synaptic connec- tions between neurons are adapted via some variant of

cality in human memory will doubtless improve tremendously. HOUSE_OVERSIGHT_013183 268 13 Local, Global and Glocal Knowledge Representation 13.6.3 Glocal Hopfield Networks The ideas in the previous section suggest that, if one wishes to construct an AGI, it is worth seriously considering using a memory with some sort o