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Convergence of a self-organizing stochastic neural network

✍ Scribed by Olivier Francois; Jacques Demongeot; Thierry Herve

Publisher: Elsevier Science
Year: 1992
Tongue: English
Weight: 451 KB
Volume: 5
Category: Article
ISSN: 0893-6080
DOI: 10.1016/s0893-6080(05)80025-5

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✦ Synopsis

In this paper, we focus on the convergence of a stochastic neural process. In this process, a "physiologically plausible" Hebb's learning rule gives rise to a self-organization phenomenon. Some preliminary results concern the asymptotic behaviour of the nework given that the update of neurons is either sequential, partially parallel, or massively parallel. We shall pay attention to the fact that Hebbian learning is closely linked to the underlying dynamics of the network. Thereafter, we shall give, within the mathematical framework of stochastic approximation, some conditions for convergence of the learning scheme.

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