Onset of 'super retrieval phase' and enhancement of the storage capacity in neural networks of nonmonotonic neurons

Abstract

Analogue neural networks of associative memory with continuous time dynamics are studied for nonmonotonic transfer functions using the method of self-consistent signal-to-noise analysis. The Hebb learning rule with unbiased random patterns is assumed for the synaptic couplings. A novel phenomenon is found to occur as a result of a phase transition concerning the property of the local field distribution. In retrieval states of the newly found phase which the authors refer to as the super retrieval phase, noise in the local field vanishes and the memory retrieval without errors ensures even for an extensive number of memory patterns stored under the local learning rule. The storage capacity is obtained as a function of the parameter representing the degree of nonmonotonicity of the transfer functions, with the result that an enhancement of the storage capacity can also occur.