Krauß P, Prebeck K, Schilling A, Metzner C (2019)
Publication Type: Journal article
Publication year: 2019
Book Volume: 13
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.
APA:
Krauß, P., Prebeck, K., Schilling, A., & Metzner, C. (2019). "Recurrence Resonance" in Three-Neuron Motifs. Frontiers in Computational Neuroscience, 13. https://dx.doi.org/10.3389/fncom.2019.00064
MLA:
Krauß, Patrick, et al. ""Recurrence Resonance" in Three-Neuron Motifs." Frontiers in Computational Neuroscience 13 (2019).
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