Stochastic resonance without tuning

Abstract

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 in an ensemble of single-electron neuromorphic devices and its application to competitive neural networks

“…When single-electron boxes are not connected to each other, electron tunneling occurs independently in each box's junction. As in the work of Collins et al [3], we apply a common input to all the boxes and calculate the summation of the outputs of all the boxes. For simplicity, we apply a common spike train S in of frequency f to all the boxes, and rather than consider practical circuits that calculate the summation of box outputs 1 .…”

“…Oya et al proposed a single-electron competitive neural network and demonstrated that the network operated correctly when T ≤ 1 K [5]. In this paper, we propose a single-electron neural circuit that can operate at high temperature by exploiting stochastic resonance (SR) in an ensemble of spiking neurons [3].…”

Inhibition of rhythmic spiking by colored noise in neural systems

“…It has been widely recognized that noise is able to facilitate the information processing and enrich the inherently stochastic dynamics of neural systems (Deco et al 2009;Destexhe and Contreras 2006). Two fundamental and maybe the most important mechanisms that the neural system may use to enhance its information transmission capability with the help of noise are stochastic resonance (Collins et al 1995;Gailey et al 1997;Chialvo et al 1997;Lee and Kim 1999;Chik et al 2001;Guo and Li 2009) and coherence resonance (Guo and Li 2009;Pikovsky and Kurths 1997;Lindner et al 2002;Li and Gao 2008;Sun et al 2008). For each mechanism, the response of the neural system greatly depends on the noise intensity, and there exists a maximal neural response at an optimal noise level.…”

Is the integrate-and-fire model good enough?—a review

“…The stochastic part of a signal is usually thought of as`noise' and is the part any system tries to get rid of. In stochastic resonance theory (Collins, Chow & Imhoff, 1995;Gammaitoni, Ha Ènggi, Jung & Marchesoni, 1998), noise is hypothesized to be useful, but an application of the theory to the neuronal system tells us that it only works inside a very limited parameter region and a carefully adjusted input signal is required (Feng & Tirozzi, 2000). Another possible role played by noise in neuronal systems has been reported recently: we (Feng & Zhang, 2001) have numerically demonstrated that with the help of noise, a neuron, such as the HH and the FHN model, can increase its efferent ®ring rate when inhibitory inputs increase.…”