Simulation of circuits demonstrating stochastic resonance

In certain dynamical systems, the addition of noise can assist the detectio n of a signal and not degrade it as normally expected. This is possible via a phenomenon termed stochastic resonance (SR), where the response of a non linear system to a subthreshold periodic input signal is optimal for some n on-zero value of noise intensity. We investigate the SR phenomenon in sever al circuits and systems. Although SR occurs in many disciplines, the sinuso idal signal by itself is not information bearing. To greatly enhance the pr acticality of SR, an (aperiodic) broadband signal is preferable. Hence, we employ aperiodic stochastic resonance (ASR) where noise can enhance the res ponse of a nonlinear system to a weak aperiodic signal. We can characterize ASR by the use of cross-correlation-based measures. Using this measure, th e ASR in a simple threshold system and in a FitzHugh-Nagumo neuronal model are compared using numerical simulations. Using both weak periodic and aper iodic signals, we show that the response of a nonlinear system is enhanced, regardless of the signal. (C) 2000 Elsevier Science Ltd. All rights reserv ed.