Sign constrained high capacity associative memory models.
Abstract
Biological neural networks do not allow the synapses to choose their own sign: excitatory or inhibitory. The consequences of imposing such a sign-constraint on the weights of the standard Hopfield associative memory architecture,
trained using perceptron like learning, are examined in this paper. The capacity and attractor
performance of these networks is empirically
investigated, with sign-constraints of varying
correlation and training sets of varying correlation. It is found that the specific correlation of the signs affects both the capacity and attractor performance in a significant way.