Stochastic resonance with differential code in feedforward network with intra-layer random connections

Abstract

We examined stochastic resonance with a differential coding scheme using a multilayer feedforward neural network which is composed of intra-layer connections. We show that the network, with random synaptic connections in each layer, encodes an input signal into a spike coherence that represents temporal differences among the inputs. We also demonstrate that both internal and external noise enhance the detection of weak signals. Finally, we discuss how the feedforward network with intra-layer random connections is similar to a membrane in its sensitivity to and amplification of a change in stimulus and suggest that the intensity of internal noise may be tuned in a real brain.