Retrieval and chaos in extremely diluted non-monotonic neural networks

M.s Mainieri and R Erichsen

Physica A: Statistical Mechanics and its Applications, 2002, vol. 311, issue 3, 581-589

Abstract: We discuss, in this paper, the dynamical properties of extremely diluted, non-monotonic neural networks. Assuming parallel updating and the Hebb prescription for the synaptic connections, a flow equation for the macroscopic overlap is derived. A rich dynamical phase diagram was obtained showing a stable retrieval phase, as well as a cycle two and chaotic behavior. Numerical simulations were performed, showing good agreement with analytical results. Furthermore, the simulations give an additional insight into the microscopic dynamical behavior during the chaotic phase. It is shown that the freezing of individual neuron states is related to the structure of chaotic attractors.

Keywords: Neural networks; Associative memory; Chaos (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:311:y:2002:i:3:p:581-589

DOI: 10.1016/S0378-4371(02)00626-X

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