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

dge 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-Formal Model of Glocal Memory 262 13.6.2 Glocal Memory in the Brain

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

ce synaptic modification causes another instance of synaptic modification, which causes another, and so forth' 113i0II. 13.5.2 Virtual Synapses and Hebbian Learning Between Assemblies Hebbian learning is conventionally formulated in terms of individual neurons, but, it can be extended naturally to assemblies via defining "virtual

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

.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; cases cose: cs s@5m5 $@EME DOES SERS EOE RS Emtes CO: 259 13.6 Glocal Memory .......00 0.0 cc eee t ene

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

ance synaptic modification causes another instance of synaptic modification, which causes another, and so forth? [Bi01]. 13.5.2 Virtual Synapses and Hebbian Learning Between Assemblies Hebbian learning is conventionally formulated in terms of individual neurons, but, it can be extended naturally to assemblies via defining “virtual