On information characteristics of sparsely encoded binary auto-associative memory

Abstract

A sparsely encoded Willshaw-like attractor neural network based on binary Hebbian synapses is investigated analytically and by computer simulations. A special inhibition mechanism which supports a constant number of active neurons at each time step is used. Informational capacity and size of attraction basins are evaluated for the single-step and the Gibson-Robinson approximations, as well as for experimental results.