AUTHOR=Krauss Patrick , Prebeck Karin , Schilling Achim , Metzner Claus 

TITLE=Recurrence Resonance” in Three-Neuron Motifs

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=Volume 13 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2019.00064

DOI=10.3389/fncom.2019.00064

ISSN=1662-5188

ABSTRACT=Stochastic resonance is a non-linear phenomenon, in which the sensitivity of signal detectors can be enhanced by adding random noise to the detector input. Here, we demonstrate that noise can also improve the information flux in recurrent neural networks. In particular, we show for the case of three-neuron motifs 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.