Input-output consistency in integrate and fire interconnected neurons

0570393 - FGÚ 2024 RIV US eng J - Článek v odborném periodiku
Lánský, Petr - Polito, F. - Sacerdote, L.
Input-output consistency in integrate and fire interconnected neurons.
Applied Mathematics and Computation. Roč. 440, 1 March (2023), č. článku 127630. ISSN 0096-3003. E-ISSN 1873-5649
Institucionální podpora: RVO:67985823
Klíčová slova: target neuron model * perfect integrate and fire * first passage time * interspike intervals * multivariate point process
Obor OECD: Applied mathematics
Impakt faktor: 4, rok: 2022
Způsob publikování: Omezený přístup
https://doi.org/10.1016/j.amc.2022.127630

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.
Trvalý link: https://hdl.handle.net/11104/0341708