Enhancing array stochastic resonance in ensembles of excitable systems

A summing network of FitzHugh–Nagumo model neurons, immersed in the background of both external noise and internal noise, is studied in the context of array stochastic resonance. An aperiodic Gaussian stimulus, assisted by collective internal array noise, stimulates the summing network for a more efficient response. This form of array stochastic resonance can be characterized by a correlation coefficient for an aperiodic input signal. Moreover, the correlation gain of the ensembles of neuronal models is investigated for finite and infinite array sizes. The nonmonotonic behavior of the correlation gain and the regions of the correlation gain beyond unity, i.e. the two main features of array SR, are demonstrated numerically and theoretically. These results suggest that certain levels of both external noise and internal noise contribute in a beneficial way to the neuronal coding strategy.

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