Eur. Phys. J. Spec. Top. (2021) 230: 2963-2972
https://doi.org/10.1140/epjs/s11734-021-00160-7

Regular Article

Modeling and characterizing stochastic neurons based on in vitro voltage-dependent spike probability functions

¹ Department of Physics, Faculty of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
² Institut de Neurosciences des Systèmes (INS, UMR 1106) Université de Aix-Marseille, Marseille, France
³ Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers University, Newark, NJ, USA
⁴ Vollum Institute, Oregon Health & Science University, Portland, OR, USA
⁵ Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, SP, Brazil
⁶ State University of New York Downstate Health Sciences University, New York, NY, USA
⁷ Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP, Brazil

^a vinicius.lima.cordeiro@gmail.com

Received: 21 November 2020
Accepted: 23 April 2021
Published online: 12 June 2021

Abstract

Neurons in the nervous system are submitted to distinct sources of noise, such as ionic-channel and synaptic noise, which introduces variability in their responses to repeated presentations of identical stimuli. This motivates the use of stochastic models to describe neuronal behavior. In this work, we characterize an intrinsically stochastic neuron model based on a voltage-dependent spike probability function. We determine the effect of the intrinsic noise in single neurons by measuring the spike time reliability and study the stochastic resonance phenomenon. The model was able to show increased reliability for non-zero intrinsic noise values, according to what is known from the literature, and the addition of intrinsic stochasticity in it enhanced the region in which stochastic-resonance is present. We proceeded to the study at the network level where we investigated the behavior of a random network composed of stochastic neurons. In this case, the addition of an extra dimension, represented by the intrinsic noise, revealed dynamic states of the system that could not be found otherwise. Finally, we propose a method to estimate the spike probability curve from in vitro electrophysiological data.