Abstract
Although typically assumed to degrade performance, random fluctuations, or noise, can sometimes improve information processing in non-linear systems. One such form of 'stochastic facilitation', stochastic resonance, has been observed to enhance processing both in theoretical models of neural systems and in experimental neuroscience. However, the two approaches have yet to be fully reconciled. Understanding the diverse roles of noise in neural computation will require the design of experiments based on new theory and models, into which biologically appropriate experimental detail feeds back at various levels of abstraction.
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Acknowledgements
This work was supported by an Australian Research Fellowship from the Australian Research Council (project number DP1093425) to M.D.M., and a Discovery Grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada to L.M.W. The authors would like to acknowledge the influence and reflections of numerous colleagues with whom they have discussed stochastic resonance. In particular, S. A. Prescott of the University of Pittsburgh, USA, for insightful dialogue on several aspects of this paper, and the anonymous referees for exhorting the necessity for new terminology. This article is dedicated to the memory of Frank E. Moss, pioneer of the study of stochastic resonance in living systems, an inspiration and a friend, who passed away January 4th 2011.
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Extended Table of Reports of Stochastic Resonance in Neurobiological Experiments (PDF 396 kb)
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Extended Table of Reports of Stochastic Resonance and Stochastic Facilitation in Neuronal Models (PDF 441 kb)
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McDonnell, M., Ward, L. The benefits of noise in neural systems: bridging theory and experiment. Nat Rev Neurosci 12, 415–425 (2011). https://doi.org/10.1038/nrn3061
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DOI: https://doi.org/10.1038/nrn3061
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