Epstein Secrets

some of the properties that the above ECAN equations display when we use an ECAN defined by them as an associative memory network in the manner of a Hopfield network. We consider a situation where the ECAN is supplied with memories via a "training" phase in which one imprints it with a series of binary

orks Economic Attention Networks (ECANs) are dynamical systems based on the propagation of STI and LTI values. They are similar in many respects to Hopfield nets, but are based on a different conceptual foundation involving the propagation of amounts of (conserved) currency rather than neural-net activa

rrently used for this are 1. (Rent} recent stimulus awarded. bettoreArupdatex (Wage) recent 2. tax =f,, where x is the distance from the current AtomSpace bounds to the center of the homeostatic range for AtomSpace funds; 3. Vi:si = — tax 23.6.1.2 Investigation of Convergence Properties of Variant 1

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

pproach all the different kinds of memory are linked together in a single meta-representation, a sort of combined semantic/neural network called the AtomSpace. It represents everything from perceptions and actions to abstract relationships and concepts and even a system's model of itself and others. When

e 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 Knowledge Representation via Cell Assemblies .................2.05- 257 13.5 Neural Foundati

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

approach all the different kinds of memory are linked together in a single meta-representation, a sort of combined semantic/neural network called the AtomSpace. It represents everything from perceptions and actions to abstract relationships and concepts and even a system’s model of itself and others. When sp