2003, Vol.6, No.4, pp.861-869
A stochastic generalization of the Hopfield analog neural networks was analytically
The phase space of the Hopfield analog
neural network was studied in the case when
synaptic coupling constants were adjusted with
the aid of the projection learning algorithm
which exploits outer products of vectors.
The shape and size of basins of attractors around
the memorized patterns are found to be
drastically dependent on the network and the
learning rule parameters.
Key words: information processing, associative memory, retrieval of memorized patterns, neural network, Wiener synaptic noise, Wiener input (threshold) noise, Ito stochastic differential equations, Lyapunov function
Acrobat PDF (158KB)
Copyright © Nonlinear Phenomena in Complex Systems. Last updated: January 9, 2004