Chaotic Oscillatory Associative Memory

Abstract

Associative memory models retrieve stored information through content-based addressing, mimicking the neural processes of animal brains. The classical Hopfield network-based models store memories as vectors of discrete values and have good storage capacity but do not consider the role of neuronal synchronization in memory storage and retrieval as observed in brains. This is addressed in phase-oscillator-based models which store memories as time-dependent phase-synchronized states, but suffer from instability and low capacity. The present study addresses these challenges through a novel chaotic oscillator-based associative memory model, by defining a phase relationship in chaotic systems and encoding memory as synchronized states of these phases. The underlying chaos in the network is shown to significantly improve both storage and retrieval and offer insights into the dynamics of memory retrieval.