Residual aperiodic stochastic resonance in a bistable dynamic system transmitting a suprathreshold binary signal

Synaptic signal transduction aided by noise in a dynamical saturating model

A generic dynamical model with saturation for neural signal transduction at the synaptic stage is presented. Analysis of this model of a synaptic pathway demonstrates its ability to give rise to stochastic resonance or improvement by noise, at this stage of signal transmission. Beyond the case of the intrinsic threshold nonlinearity of the neuron response, the results extend the feasibility of stochastic resonance to neural saturating dynamics at the synaptic stage. The present results also constitute the exposition of a new type of nonlinear (saturating) dynamics capable of stochastic resonance.

Comparison of aperiodic stochastic resonance in a bistable system realized by adding noise and by tuning system parameters

Two methods of realizing aperiodic stochastic resonance (ASR) by adding noise and tuning system parameters in a bistable system, after a scale transformation, can be compared in a real parameter space. In this space, the resonance point of ASR via adding noise denotes the extremum of a line segment, whereas the method of tuning system parameters presents the extrema of a parameter plane. We demonstrate that, in terms of the system performance, the method of tuning system parameters takes the precedence of the approach of adding noise for an adjustable bistable system. Besides, adding noise can be viewed as a specific case of tuning system parameters. Further research shows that the optimal system found by tuning system parameters may be subthreshold or suprathreshold, and the conventional ASR effects might not occur in some suprathreshold optimal systems.