Inverse stochastic resonance in networks of spiking neurons

Muhammet Uzuntarla; Ernest Barreto; Joaquin J Torres

doi:10.1371/journal.pcbi.1005646

Inverse stochastic resonance in networks of spiking neurons

PLoS Comput Biol. 2017 Jul 10;13(7):e1005646. doi: 10.1371/journal.pcbi.1005646. eCollection 2017 Jul.

Authors

Muhammet Uzuntarla¹, Ernest Barreto², Joaquin J Torres³

Affiliations

¹ Department of Biomedical Engineering, Bulent Ecevit University, Engineering Faculty, Zonguldak, Turkey.
² Department of Physics and Astronomy and The Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia, United States of America.
³ Department of Electromagnetism and Physics of Matter, and Institute Carlos I for Theoretical and Computational Physics, University of Granada, Granada, Spain.

Abstract

Inverse Stochastic Resonance (ISR) is a phenomenon in which the average spiking rate of a neuron exhibits a minimum with respect to noise. ISR has been studied in individual neurons, but here, we investigate ISR in scale-free networks, where the average spiking rate is calculated over the neuronal population. We use Hodgkin-Huxley model neurons with channel noise (i.e., stochastic gating variable dynamics), and the network connectivity is implemented via electrical or chemical connections (i.e., gap junctions or excitatory/inhibitory synapses). We find that the emergence of ISR depends on the interplay between each neuron's intrinsic dynamical structure, channel noise, and network inputs, where the latter in turn depend on network structure parameters. We observe that with weak gap junction or excitatory synaptic coupling, network heterogeneity and sparseness tend to favor the emergence of ISR. With inhibitory coupling, ISR is quite robust. We also identify dynamical mechanisms that underlie various features of this ISR behavior. Our results suggest possible ways of experimentally observing ISR in actual neuronal systems.

MeSH terms

Action Potentials / physiology*
Algorithms
Computational Biology
Humans
Models, Neurological*
Nerve Net / physiology*
Neurons / physiology*
Stochastic Processes

Grants and funding

MU acknowledges financial support from the Granada Excellence Network of Innovation Laboratories (GENIL). JJT acknowledges support from project FIS2013-43201-P, which is funded by the Spanish Ministry of Economy and Competitiveness (MINECO) and by the European Regional Development’s Founds (FEDER). Publication of this article was funded in part by the George Mason University Libraries Open Access Publishing Fund and Bulent Ecevit University’s Scientific Research Projects department. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.