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

TL;DR

This paper introduces a stochastic neuron model based on voltage-dependent spike probability functions, demonstrating how intrinsic noise influences spike reliability, stochastic resonance, and network dynamics, with a method to estimate these probabilities from experimental data.

Contribution

It presents a novel stochastic neuron model incorporating intrinsic noise and provides a method to estimate spike probability curves from in vitro data, advancing understanding of neuronal variability.

Findings

Intrinsic noise increases spike time reliability.

Intrinsic stochasticity enhances stochastic resonance regions.

Network dynamics reveal new states with added noise.

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.