Temporal segmentation and selective attention in the stochastic oscillator neural network

Abstract

A stochastic oscillator neural network (STONN) model of the Hopfield-type memory is proposed for the pattern segmentation tasks, that exploits temporal dynamics of the stochastic nonlinear oscillators. For an input pattern which is an overlapped superposition of several stored patterns the proposed model network is shown to be capable of segmenting out each pattern one after another as the network evolves its temporal dynamics. The temporal segmentation attains its optimal performance at an intermediate noise intensity and the performance becomes improved as the coupling strength between oscillators increases. A mechanism for the selective attention is also introduced in the STONN by controlling the level of noise applied to the most salient pattern and by adopting the inhibition-of-return into the patterns that have been segmented before.