Neighborhood induced stochastic resonance

Spatial interactions over a local neighborhood between neurons is common in many models of neural networks. The spatial interaction is often shaped to allow for improved response in the application for which the network is constructed (e.g. signal detection or sensory segmentation). In this paper, we study an additional e ect that can occur when such spatial interactions are imposed. Speci cally, we nd that the net input received by a neuron from neighboring neurons can induce stochastic resonance thereby increasing the signalto-noise ratio of the neuron output. We also nd that stochastic resonance is enhanced with increasing variance between the neighborhood signals received by the neuron. Thus the net signal received by a neuron because of neighborhood neurons plays a role similar to that played by noise in a classical stochastic resonance situations. We believe that these results when combined with the more traditional use of spatial interaction motivate the design of new and enhanced neural detectors.