Modeling the implications of nitric oxide dynamics on information transmission: An automata networks approach
The result's identifiers
Result code in 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>
Alternative codes found
RIV/68407700:21730/23:00374363
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Modeling the implications of nitric oxide dynamics on information transmission: An automata networks approach
Original language description
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.
Czech name
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Czech description
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Classification
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
AIMS Mathematics
ISSN
2473-6988
e-ISSN
2473-6988
Volume of the periodical
8
Issue of the periodical within the volume
12
Country of publishing house
US - UNITED STATES
Number of pages
40
Pages from-to
30142-30181
UT code for WoS article
001108206300001
EID of the result in the Scopus database
2-s2.0-85175864028