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

Mark D. McDonnell, Nicolangelo Iannella, Minh Son To, Henry C. Tuckwell, Jürgen Jost, Boris S. Gutkin, Lawrence M. Ward

    Research output: Contribution to journalReview articlepeer-review

    18 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)35-71
    Number of pages37
    JournalNetwork: Computation in Neural Systems
    Issue number2
    Publication statusPublished - Jun 2015


    • Neuron models
    • Noise
    • Stochastic facilitation
    • Stochastic resonance


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