Modeling the implications of nitric oxide dynamics on information transmission: An automata networks approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F23%3A43927711" target="_blank" >RIV/60461373:22340/23:43927711 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/23:00374363

Výsledek na webu

<a href="https://www.researchgate.net/publication/375484832_Modeling_the_implications_of_nitric_oxide_dynamics_on_information_transmission_An_automata_networks_approach" target="_blank" >https://www.researchgate.net/publication/375484832_Modeling_the_implications_of_nitric_oxide_dynamics_on_information_transmission_An_automata_networks_approach</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3934/math.20231541" target="_blank" >10.3934/math.20231541</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modeling the implications of nitric oxide dynamics on information transmission: An automata networks approach

Popis výsledku v původním jazyce

Nitric oxide (NO) is already recognized as an important signaling molecule in the brain. It diffuses easily and the nervous cell’s membrane is permeable to NO. The information transmission is three-dimensional, which is different from synaptic transmission. NO operates in two different ways: Close and specific at the synapses of neurons, and as a volumetric transmitter sending signals to various targets, regardless of their anatomy, connectivity or function, when multiple nearby sources act simultaneously. These modes of operation seem to be the basis by which NO is involved in many central mechanisms of the brain, such as learning, memory formation, brain development and synaptogenesis. This work focuses on the effect of NO dynamics on the environment through which it diffuses, using automata networks. We study their implications in the formation of complex functional structures in the volume transmission (VT), which are necessary for the synchronous functional recruitment of neuronal populations. We qualitatively and quantitatively analyze the proposed model regarding these characteristics through the concepts of entropy and mutual information. The proposed deterministic model allows the incorporation of fuzzy dynamics. With that, a generalized model based on fuzzy automata networks can be provided. This allows the generation and diffusion processes of NO to be arbitrarily produced and maintained over time. This model can accommodate arbitrary processes in decision-making mechanisms and can be part of a complete formal VT framework in the brain and artificial neural networks. © 2023 the Author(s), licensee AIMS Press. is an open access article distributed under.

Název v anglickém jazyce

Modeling the implications of nitric oxide dynamics on information transmission: An automata networks approach

Popis výsledku anglicky

Nitric oxide (NO) is already recognized as an important signaling molecule in the brain. It diffuses easily and the nervous cell’s membrane is permeable to NO. The information transmission is three-dimensional, which is different from synaptic transmission. NO operates in two different ways: Close and specific at the synapses of neurons, and as a volumetric transmitter sending signals to various targets, regardless of their anatomy, connectivity or function, when multiple nearby sources act simultaneously. These modes of operation seem to be the basis by which NO is involved in many central mechanisms of the brain, such as learning, memory formation, brain development and synaptogenesis. This work focuses on the effect of NO dynamics on the environment through which it diffuses, using automata networks. We study their implications in the formation of complex functional structures in the volume transmission (VT), which are necessary for the synchronous functional recruitment of neuronal populations. We qualitatively and quantitatively analyze the proposed model regarding these characteristics through the concepts of entropy and mutual information. The proposed deterministic model allows the incorporation of fuzzy dynamics. With that, a generalized model based on fuzzy automata networks can be provided. This allows the generation and diffusion processes of NO to be arbitrarily produced and maintained over time. This model can accommodate arbitrary processes in decision-making mechanisms and can be part of a complete formal VT framework in the brain and artificial neural networks. © 2023 the Author(s), licensee AIMS Press. is an open access article distributed under.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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

AIMS Mathematics

ISSN

2473-6988

e-ISSN

2473-6988

Svazek periodika

8

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

40

Strana od-do

30142-30181

Kód UT WoS článku

001108206300001

EID výsledku v databázi Scopus

2-s2.0-85175864028