Stochastic resonance (SR) is a phenomenon wherein the response of a nonlinear system to a weak periodic input signal is optimized by the presence of a particular, non-zero level of noise. SR has been proposed as a means for improving signal detection in a wide variety of systems, including superconducting quantum interference devices, and may be used in some natural systems such as sensory neurons. But for SR to be effective in a single-unit system (such as a sensory neuron or a single ion channel), the optimal intensity of the noise must be adjusted as the nature of the signal to be detected changes. This has been thought to impose a limitation on the practical and natural uses of SR. Here we show that the ability of a summing network of excitable units to detect a range of weak (sub-threshold) signals (either periodic or aperiodic) can be optimized by a fixed level of noise, irrespective of the nature of the input signal. We also show that this noise does not significantly degrade the ability of the network to detect suprathreshold signals. Thus, large nonlinear networks do not suffer from the limitations of SR in single units, and might be able to use a single noise level, such as that provided by the intrinsic noise of the individual components, to enhance the system's sensitivity to weak inputs. This suggests a functional role for neuronal noise in sensory systems.

Stochastic resonance (SR) is a phenomenon wherein the response of a nonlinear system to a weak periodic input signal is optimized by the presence of a particular level of noise. Here we present a new method and theory for characterizing SR-type behavior in excitable systems with aperiodic inputs. These novel developments demonstrate that noise can serve to enhance the response of a nonlinear system to a weak input signal, regardless of whether the signal is periodic or aperiodic.

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Stochastic resonance ͑SR͒ is a phenomenon wherein the response of a nonlinear system to a weak periodic input signal is optimized by the presence of a particular level of noise. Recently, we presented a method and theory for characterizing SR-type behavior in excitable systems with aperiodic ͑i.e., broadband͒ input signals ͓Phys. Rev. E 52, R3321͑1995͔͒. We coined the term aperiodic stochastic resonance ͑ASR͒ to describe this general type of behavior. In that earlier study, we demonstrated ASR in the FitzHugh-Nagumo neuronal model. Here we demonstrate ASR in three additional systems: a bistable-well system, an integrate-and-fire neuronal model, and the Hodgkin-Huxley ͑HH͒ 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 excitable membranes. This work clearly shows that SR-type behavior is not limited to systems with periodic inputs. Thus, in general, noise can serve to enhance the response of a nonlinear system to a weak input signal, regardless of whether the signal is periodic or aperiodic.

1. Aperiodic stochastic resonance (ASR) is a phenomenon wherein the response of a nonlinear system to a weak aperiodic input signal is optimized by the presence of a particular, nonzero level of noise. Our objective was to demonstrate ASR experimentally in mammalian cutaneous mechanoreceptors. 2. Experiments were performed on rat slowly adapting type 1 (SA1) afferents. Each neuron was subjected to a perithreshold aperiodic stimulus plus noise. The variance of the noise was varied between trials. The coherence between the aperiodic input stimulus and the response of each SA1 afferent was computed. 3. Of the 12 neurons tested, 11 showed clear ASR behavior: as input noise variance was increased, the stimulus-response coherence rapidly increased to a peak and then slowly decreased. These findings were in contrast with those for the average firing rate, which increased monotonically as a function of input noise variance. 4. This work shows that noise can serve to enhance the response of a sensory neuron to a perithreshold aperiodic input signal. These results suggest a possible functional role for input noise in sensory systems. These findings also indicate that it may be possible to introduce noise artificially into sensory neurons to improve their abilities to detect arbitrary weak signals.