Abstract

We present a discussion on the dynamics of a higher order network of McColluch-Pitts neurons connected via Hebbian-type rules. Both first and second order synaptic connections are randomly cut off to model observed incomplete connectivity in real neurophysiological systems and thereby we obtain an exact solution of the network dynamics. We find a variety of dynamical behavior such as stable retrievals, oscillations and chaos in the neural network. We show that the rescaled noise level which represents the combined effects of the random synaptic dilution, intereference between stored patterns, and additional background noise, acts as the bifurcation parameter in the present system.

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