The silencing of neuronal activity by noise and the phenomenon of inverse stochastic resonance
Boris Gutkin, Jürgen Jost, and Henry Tuckwell
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Submission date: 07. May. 2008
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Neurons in the central nervous system are affected by complex and noisy signals due to fluctuations in their cellular environment and in the inputs they receive from many other cells. Such noise usually increases the probability that a neuron will send out a signal to its target cells. In stochastic resonance, which occurs in many physical and biological systems, an optimal response is found at a particular noise amplitude. We have found that in a classical neuronal model the opposite can occur - that noise can subdue or turn off repetitive neuronal activity in both single cells and networks of cells. Recent experiments on regularly firing neurons with noisy inputs confirm these predictions. Surprisingly, we find that in some cases there is a noise level at which the response is a minimum, a phenomenon which is called inverse stochastic resonance. Suppression of rhythmic behavior by noise and inverse stochastic resonance are predicted to occur not only in neuronal systems but more generally in diverse nonlinear dynamical systems where a stable limit cycle is attainable from a stable rest state.