On information characteristics of sparsely encoded binary auto-associative memory
2002
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.
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