Recurrence Resonance" in Three-Neuron Motifs.

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ID: 56620
2019
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Ranked #2 of 9 articles by views in Frontiers in computational neuroscience

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Abstract
Stochastic Resonance (SR) and Coherence Resonance (CR) are non-linear phenomena, in which an optimal amount of noise maximizes an objective function, such as the sensitivity for weak signals in SR, or the coherence of stochastic oscillations in CR. Here, we demonstrate a related phenomenon, which we call "Recurrence Resonance" (RR): noise can also improve the information flux in recurrent neural networks. In particular, we show for the case of three-neuron motifs with ternary connection strengths that the mutual information between successive network states can be maximized by adding a suitable amount of noise to the neuron inputs. This striking result suggests that noise in the brain may not be a problem that needs to be suppressed, but indeed a resource that is dynamically regulated in order to optimize information processing.
Reference Key
krauss2019recurrencefrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Krauss, Patrick;Prebeck, Karin;Schilling, Achim;Metzner, Claus;
Journal Frontiers in computational neuroscience
Year 2019
DOI
10.3389/fncom.2019.00064
URL
Keywords
noise motifs entropy recurrent neural networks coherence resonance mutual information stochastic resonance

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