Input-output consistency in integrate and fire interconnected neurons

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985823%3A_____%2F23%3A00570393" target="_blank" >RIV/67985823:_____/23:00570393 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.amc.2022.127630" target="_blank" >https://doi.org/10.1016/j.amc.2022.127630</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.amc.2022.127630" target="_blank" >10.1016/j.amc.2022.127630</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Input-output consistency in integrate and fire interconnected neurons

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Input-output consistency in integrate and fire interconnected neurons

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Mathematics and Computation

ISSN

0096-3003

e-ISSN

1873-5649

Svazek periodika

440

Číslo periodika v rámci svazku

1 March

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

127630

Kód UT WoS článku

000885383800004

EID výsledku v databázi Scopus

2-s2.0-85140991511