Epstein Secrets

arding the representation of knowledge using attractor neural nets. It is a mix of well-established fact with more speculative material. 13.4.1 The Hopfield neural net model Hopfield networks Iliop821 are attractor neural networks often used as associative memories. A Hopfield network with N neurons can

tation of glocal memory in attractor neural net systems • Chapter 23 presents Glocal Economic Attention Networks (ECANs), rough analogues of glocal Hopfield nets that play a central role in CogPrime. Our hypothesis of the potential general importance of glocality as a property of memory, systems (beyond

moved without significantly impacting the net- work's capacity or dynamics. Our experimental work uses sparse Hopfield networks. 13.4.1.1 Palimpsest Hopfield nets with a modified learning rule In JSV99J a new learning rule is presented, which both increases the Hopfield network capacity and turns it into

the possible emergence of structures characterizing inter-cognitive reflection. 279 EFTA00624055 280 15 Emergent Networks of Intelligence 15.2 Small World Networks One simple but potentially useful observation about CogPrime Atomspaces is that they are generally going to be small world networks Inuc03], rather

garding the representation of knowledge using attractor neural nets. It is a mix of well-established fact with more speculative material. 13.4.1 The Hopfield neural net model Hopfield networks [Hop82] are attractor neural networks often used as associative memories. A Hopfield network with N neurons can b

ntation of glocal memory in attractor neural net systems e Chapter 23 presents Glocal Economic Attention Networks (ECANs), rough analogues of glocal Hopfield nets that play a central role in CogPrime. Our hypothesis of the potential general importance of glocality as a property of memory systems (beyond j

moved without significantly impacting the net- work’s capacity or dynamics. Our experimental work uses sparse Hopfield networks. 13.4.1.1 Palimpsest Hopfield nets with a modified learning rule In [SV99] a new learning rule is presented, which both increases the Hopfield network capacity and turns it into

sible emergence of structures characterizing inter-cognitive reflection. 279 HOUSE_OVERSIGHT_013195 280 15 Emergent Networks of Intelligence 15.2 Small World Networks One simple but potentially useful observation about CogPrime Atomspaces is that they are generally going to be small world networks [Buc03], rather

th 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 Neural Foundations of Learning 258 13.5.1 Hebbian Learni

ve pattern recognition as well as static pattern recognition. Audition likely utilizes a similar hierarchy. Olfaction may use something more like a Hopfield attractor neural network, as described in Chapter 13. The networks corresponding to different sense modalities have multiple cross-linkages, more a

hs of Intelligent Systems 274 14.4.1 SMEPH Principles in CogPrime 276 15 Emergent Networks of Intelligence 279 15.1 Introduction 279 15.2 Small World Networks 280 15.3 Dual Network Structure 281 15.3.1 Hierarchical Networks 281 15.3.2 Associative, Heterarchical Networks 282 15.3.3 Dual Networks

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tive pattern recognition as well as static pattern recognition. Audition likely utilizes a similar hierarchy. Olfaction may use something more like a Hopfield attractor neural network, as described in Chapter 13. The networks corresponding to different sense modalities have multiple cross-linkages, more at

rks of Intelligence..............00... 002. c eee eee 279 L5el JPOAWEtIOH, «205 seme emees Cees EMNOs LE SMEG MEME SEMS SERS EM E!s dees ema 279 15.2 Small World Networks ........0..000.0 0000 c cette 280 15.3 Dual Network Structure .......0..0.0 000 ete 281 15.3.1 Hierarchical Networks...........0.00.000 00 eee ee 281