Noise-driven image processing based on array-enhanced stochastic resonance with population heterogeneity

We found a new class of stochastic resonance (SR) in a simple neural network that consists of i) photoreceptors generating nonuniform outputs for common inputs with random offsets, ii) an ensemble of noisy McCulloch-Pitts (MP) neurons each of which has random threshold values in the temporal domain, iii) local connections between the photoreceptors and the MP neurons with variable receptive fields (RFs), iv) the output cells, and v) local connections between the MP neurons and output cells. We calculated correlation values between the inputs and the outputs as a function of the RF size and intensities of the random components in photoreceptors and the MP neurons. We show the existence of ¿optimal noise intensities¿ of the MP neurons under the nonidentical photoreceptors and ¿nonzero optimal RF sizes¿, which indicated that optimal correlation values of this SR model were determined by two critical parameters; noise intensities (well-known) and RF sizes as a new parameter.