APERIODIC STOCHASTIC RESONANCE

Stochastic resonance (SR) is a phenomenon wherein the response of a no nlinear system to a weak periodic input signal is optimized by the pre sence of a particular level of noise. Recently, we presented a method and theory for characterizing SR-type behavior in excitable systems wi th aperiodic (i.e., broadband) input signals [Phys. Rev. E 52, R3321(1 995)]. We coined the term aperiodic stochastic resonance (ASR) to desc ribe this general type of behavior. In that earlier study, we demonstr ated ASR in the FitzHugh-Nagumo neuronal model. Here we demonstrate AS R in three additional systems: a bistable-well system, an integrate-an d-fire neuronal model, and the Hodgkin-Huxley (HH) neuronal model. We present computational and theoretical results for each system. In the context of the HH model, we develop a general theory for ASR in excita ble membranes. This work clearly shows that SR-type behavior is not li mited to systems with periodic inputs. Thus, in general, noise can ser ve to enhance the response of a nonlinear system to a weak input signa l, regardless of whether the signal is periodic or aperiodic.