Input-output consistency in integrate and fire interconnected neurons
Abstract
Interspike intervals describe the output of neurons. Signal transmission in a neuronal network implies that the output of some neurons becomes the input of others. The output should reproduce the main features of the input to avoid a distortion when it becomes the input of other neurons, that is input and output should exhibit some sort of consistency. In this paper, we consider the question: how should we mathematically characterize the input in order to get a consistent output? Here we interpret the consistency by requiring the reproducibility of the input tail behaviour of the interspike intervals distributions in the output. Our answer refers to a system of interconnected neurons with stochastic perfect integrate and fire units. In particular, we show that the class of regularly-varying vectors is a possible choice to obtain such consistency. Some further necessary technical hypotheses are added.
Keywords
Cite
@article{arxiv.2206.12324,
title = {Input-output consistency in integrate and fire interconnected neurons},
author = {Petr Lansky and Federico Polito and Laura Sacerdote},
journal= {arXiv preprint arXiv:2206.12324},
year = {2022}
}