Input-output consistency in integrate and fire interconnected neurons

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Input-output consistency in integrate and fire interconnected neurons
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10102 - Applied mathematics

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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