Shift-invariant associative memory based on homogeneous neural networks
This paper proposes homogeneous neural networks (HNNs), in which each neuron has identical weights. HNNs can realize shift-invariant associative memory, that is, HNNs can associate not only a memorized pattern but also its shifted ones. The transition property of HNNs is analyzed by the statistical method. We show the probability that each neuron outputs correctly and the error-correcting ability. Further, we show that HNNs cannot memorize over the number, ((m)(k))/2m log m, of patterns, where m is the number of neurons and k is the order of connections.
shift-invariant associative memory homogeneous neural networks memory capacity