Recurrence Resonance” in Three-Neuron Motifs

Frontiers in Computational Neuroscience

Claus Metzner¹  Karin Prebeck³  Achim Schilling³  Patrick Krauss³ 

[1]Biophysics Group, Department of Physics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany

[2]Cognitive Computational Neuroscience Group at the Chair of English Philology and Linguistics, Department of English and American Studies, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany

[3]Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany

关键词: stochastic resonance;    coherence resonance;    recurrent neural networks;    entropy;    mutual information;    motifs;   

DOI  :  10.3389/fncom.2019.00064

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown