Chaotic dynamics of high-order neural networks
Copyright policy )Chaotic dynamics of high-order neural networks
The dynamics of an extremely diluted neural network with high order synapses acting as corrections to the Hopfield model is investigated. As in the fully connected case, the high order terms may strongly improve the storage capacity of the system. The dynamics displays a very rich behavior, and in particular a new chaotic phase emerges depending on the weight of the high order connections $ε$, the noise level $T$ and the network load defined as the rate between the number of stored patterns and the mean connectivity per neuron $α=P/C$.
16 pages, LaTeX (11 figures upon request), IFUFRGS-JJA-9303
chaotic dynamics, Condensed Matter (cond-mat), FOS: Physical sciences, Condensed Matter, Nonlinear Sciences - Chaotic Dynamics, Strange attractors, chaotic dynamics of systems with hyperbolic behavior, Neural nets applied to problems in time-dependent statistical mechanics, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, Neurons and Cognition (q-bio.NC), Chaotic Dynamics (nlin.CD), Neural networks, multineuron interaction
chaotic dynamics, Condensed Matter (cond-mat), FOS: Physical sciences, Condensed Matter, Nonlinear Sciences - Chaotic Dynamics, Strange attractors, chaotic dynamics of systems with hyperbolic behavior, Neural nets applied to problems in time-dependent statistical mechanics, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, Neurons and Cognition (q-bio.NC), Chaotic Dynamics (nlin.CD), Neural networks, multineuron interaction
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