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

Document Type

Article

Publication Date

10-1-2021

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.

Identifier

85107768793 (Scopus)

Publication Title

European Physical Journal Special Topics

External Full Text Location

https://doi.org/10.1140/epjs/s11734-021-00160-7

e-ISSN

19516401

ISSN

19516355

First Page

2963

Last Page

2972

Issue

14-15

Volume

230

Grant

2013/07699-0

Fund Ref

Deutsche Forschungsgemeinschaft

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