Noise induced tuned decrease in firing in neural models: inverse stochastic resonance

Neurons in the central nervous system are influenced by complex and noisy signals due to fluctuations in their cellular environment and in the inputs they receive from many other cells [1,2]. Such noise often increases the probability that a neuron will send out a signal to its target cells [2-5]. In stochastic resonance, which occurs in many physical and biological systems, an optimal response is found at a particular noise amplitude [6-8]. Remarkably, in classical neuronal models [9,10] and experimentally [11,12] it has also been found that noise can subdue or turn off repetitive neuronal activity. Surprisingly, we find that in some cases there is a noise level at which the quenching is at a maximum and the cells response is at a minimum. We call this tuning phenomenon inverse stochastic resonance and argue that it requires hysteresis between a stable rest point and a limit cycle. KEY WORDS