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, x(i)-->g( summation operator jT(ij)x(j)), that describes the network evolution. Maximum capacity is reached when the neuron dynamics are subdivided into two non-overlapping "erratic bands" around points x(i)=+/-1. The capacity improvements are explained in terms of the energy function associated with the network dynamics.
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Istituto per la Ricerca Scientifica e Tecnologica, I-38050, Povo (Trento), Italy