Suprathreshold Stochastic Resonance in a Neuronal Network Model: a Possible Strategy for Sensory Coding
The possible mechanism to explain the dynamics of the transduction in sensory neurons is investigated. We consider a parallel array of noisy FitzHugh-Nagumo model neurons, subject to a common input signal. The information transmission of the signal through the array is studied as a function of the internal noise intensity. The threshold of each neuron is set suprathreshold with respect to the input signal. A form of stochastic resonance, termed suprathreshold stochastic resonance (SSR), which has recently been observed in a network of threshold devices  is also found to occur in the FHN array. It is demonstrated that significant information gain, over and above that attainable in a single FHN element, can be achieved via the SSR effect. These information gains are still achievable under the assumption that the thresholds are fully adjustable.
KeywordsPower Spectral Density Noise Intensity Stochastic Resonance Average Mutual Information Internal Noise
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