Information model of resonance phenomena in brain neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F18%3A00326882" target="_blank" >RIV/68407700:21260/18:00326882 - isvavai.cz</a>

Výsledek na webu

<a href="http://nnw.cz/doi/2018/NNW.2018.28.014.pdf" target="_blank" >http://nnw.cz/doi/2018/NNW.2018.28.014.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2018.28.014" target="_blank" >10.14311/NNW.2018.28.014</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Information model of resonance phenomena in brain neural networks

Popis výsledku v původním jazyce

The paper presents an information model for representation of brain linear and nonlinear resonance phenomena based on information nullors. In the brain functions the rhythms and quasi periodicity of processes in neural networks play the outstanding (significant) role. It is why adaptive resonance theory (ART) including resonant effects has been studied for a long time by many authors. The periodicity in the transfers of signals between the long-term memory (LTM) and short-term memory (STM) creates a possibility of resonance system structure. LTM with information content representing expectations and STM covering sensory information in resonance process offer effective learning. Nonlinear adaptive resonance creates conditions for new knowledge, or inventory observation. In the paper this feature is newly modelled by an information gyrator that best fits these linear and non-linear phenomena.

Název v anglickém jazyce

Information model of resonance phenomena in brain neural networks

Popis výsledku anglicky

The paper presents an information model for representation of brain linear and nonlinear resonance phenomena based on information nullors. In the brain functions the rhythms and quasi periodicity of processes in neural networks play the outstanding (significant) role. It is why adaptive resonance theory (ART) including resonant effects has been studied for a long time by many authors. The periodicity in the transfers of signals between the long-term memory (LTM) and short-term memory (STM) creates a possibility of resonance system structure. LTM with information content representing expectations and STM covering sensory information in resonance process offer effective learning. Nonlinear adaptive resonance creates conditions for new knowledge, or inventory observation. In the paper this feature is newly modelled by an information gyrator that best fits these linear and non-linear phenomena.

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

<a href="/cs/project/EF15_003%2F0000466" target="_blank" >EF15_003/0000466: Umělá inteligence a uvažování</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

—

Svazek periodika

28

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

15

Strana od-do

225-239

Kód UT WoS článku

000440210500003

EID výsledku v databázi Scopus

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