Input-output behaviour of a model neuron with alternating drift

TL;DR

This paper investigates the input-output behavior of a Wiener neuronal model with alternating drift, analyzing firing densities through simulations under various random and deterministic input change distributions.

Contribution

It introduces a detailed simulation-based analysis of a Wiener neuronal model with alternating drift, comparing different input change distributions.

Findings

Firing densities vary significantly with different input change distributions.

Simulation results highlight differences between stochastic and deterministic input effects.

The model's response to sinusoidal input is used as a benchmark for comparison.

Abstract

The input-output behaviour of the Wiener neuronal model subject to alternating input is studied under the assumption that the effect of such an input is to make the drift itself of an alternating type. Firing densities and related statistics are obtained via simulations of the sample-paths of the process in the following three cases: the drift changes occur during random periods characterized by (i) exponential distribution, (ii) Erlang distribution with a preassigned shape parameter, and (iii) deterministic distribution. The obtained results are compared with those holding for the Wiener neuronal model subject to sinusoidal input