Storage capacity of non-monotonic neurons
Non-monotonic neurons have been shown to enhance the storage capacity of auto-associative memories. This work investigates the retrieval capacities of different types of non-monotonic neurons. It is found that storage capacity is maximized when the neuron response is a function with well defined geometrical characteristics. Numerical experiments demonstrate that storage capacity is directly related to the dynamical property of the iterative map, that describes the network evolution. Maximum capacity is reached when the neuron dynamics are subdivided into two non-overlapping erratic bands around points xi=1. The capacity improvements are explained in terms of the energy function associated with the network dynamics.
storage capacity non-monotonic neurons network dynamics hopfield networks associative memories