A review of methods for identifying stochastic resonance in simulations of single neuron models

Abstract

Stochastic resonance (SR) is said to be observed when the presence of noise in a nonlinear system enables an output signal from the system to better represent some feature of an input signal than it does in the absence of noise. The effect has been observed in models of individual neurons, and in experiments performed on real neural systems. Despite the ubiquity of biophysical sources of stochastic noise in the nervous system, however, it has not yet been established whether neuronal computation mechanisms involved in performance of specific functions such as perception or learning might exploit such noise as an integral component, such that removal of the noise would diminish performance of these functions. In this paper we revisit the methods used to demonstrate stochastic resonance in models of single neurons. This includes a previously unreported observation in a multicompartmental model of a CA1-pyramidal cell. We also discuss, as a contrast to these classical studies, a form of 'stochastic facilitation', known as inverse stochastic resonance. We draw on the reviewed examples to argue why new approaches to studying 'stochastic facilitation' in neural systems need to be developed.

Emergence of spike correlations in periodically forced excitable systems

“…Some important properties of ISI sequences are related to coherence and stochastic resonance phenomena. Coherence resonance refers to enhanced spike regularity under an optimal level of noise [1], while stochastic resonance refers to enhanced detection and transmission of subthreshold time-varying signals, also under an optimal level of noise [2][3][4][5].…”

Noise induced quiescence of epileptic spike generation in patients with epilepsy

“…As was shown in Results, the ISI-spectral parameter curves showed a maxima at a log normalized spectral parameter of around −1 for the different frequency bands indicating that the findings were at least partly frequency independent. The maxima is not typical for classical stochastic resonance which is frequency dependent (McDonnell et al 2015 ). Assuming that the spectral features were the result of fluctuations around a stable point the independence on the frequency band would be expected as discussed in Section 2.4.1 .…”

Inhibition enhances the coherence in the Jacobi neuronal model

“…Conversely, at large noise regimes, the membrane potential fluctuations are dominated by the noise, the firings are frequent and highly irregular. Coherence resonance refers to the phenomenon that occurs at intermediate intensities of the noise and for which the firings become more regular than at the low and high noise intensity scenarios [9,13,16,17]. In the case of the Jacobi neuronal model, the amplitude of the noise depends linearly on the input rates [30,31,32], so the approach presented in this paper is in the style of coherence resonance, but conceptually different.…”

“…Other examples are the cross-correlation coefficient, [11], for the integrate-and-fire or Hodgkin-Huxley neuron models or the mutual information between the input and the output, [15]. However, the most commonly used measures are evaluated from the power spectrum both for single neurons [12,14,16,17,18,19,20] and recently for neuronal networks [21,22,23]. The metric we use here as an indicator of the stochastic facilitation is the degree of coherence, β.…”