  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 1,2. Such noise usually 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 6-9. 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 10,11. 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.
