Year: 2017
Type: article
Abstract: Stochastic resonance is a phenomenon in which noise enhances the response of a system to an input signal. The brain is an example of a system that has to detect and transmit signals in a noisy environment, suggesting that it is a good candidate to take advantage of stochastic resonance. In this work, we aim to identify the optimal levels of noise that promote signal transmission through a simple network model of the human brain. Specifically, using a dynamic model implemented on an anatomical brain network (connectome), we investigate the similarity between an input signal and a signal that has traveled across the network while the system is subject to different noise levels. We find that non-zero levels of noise enhance the similarity between the input signal and the signal that has traveled through the system. The optimal noise level is not unique; rather, there is a set of parameter values at which the information is transmitted with greater precision, this set corresponds to the parameter values that place the system in a critical regime. The multiplicity of critical points in our model allows it to adapt to different noise situations and remain at criticality.
Source: Scientific Reports
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funders: National Science Foundation, École Polytechnique Fédérale de Lausanne, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Université de Genève, Université de Lausanne, Centre d'Imagerie BioMédicale, Centre Hospitalier Universitaire Vaudois, Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México
OpenAlex ID: W3101537112