Artificial Neuron with Homeostatic Behaviour

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Artificial Neuron with Homeostatic Behaviour

Popis výsledku v původním jazyce

Homeostasis is a property of a system that regulates its internal environment in order to maintain stable conditions. It is typical for any for biological systems and therefore also for neural cell. This paper presents a way how to use the idea of homeostasis in the field of artificial neural networks. The artificial neuron is here considered as an information homeostat. The state of equilibrium means a situation when the level of computational utility reaches its maximum. This idea is based on the presumption that the neuron has two inputs: first, the output of the neurons in the previous layer through its dendrites, and secondly the part of its output signal that is returned from the folowing layer through its axon. The presented idea is inspired bythe fact that the biological neuron can know which part of its output energy is accepted by other neurons. Several methods of the learning are presented.

Název v anglickém jazyce

Artificial Neuron with Homeostatic Behaviour

Popis výsledku anglicky

Homeostasis is a property of a system that regulates its internal environment in order to maintain stable conditions. It is typical for any for biological systems and therefore also for neural cell. This paper presents a way how to use the idea of homeostasis in the field of artificial neural networks. The artificial neuron is here considered as an information homeostat. The state of equilibrium means a situation when the level of computational utility reaches its maximum. This idea is based on the presumption that the neuron has two inputs: first, the output of the neurons in the previous layer through its dendrites, and secondly the part of its output signal that is returned from the folowing layer through its axon. The presented idea is inspired bythe fact that the biological neuron can know which part of its output energy is accepted by other neurons. Several methods of the learning are presented.

Druh

D - Stať ve sborníku

CEP obor

BD - Teorie informace

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název statě ve sborníku

The 2010 European Simulation and modelling conference

ISBN

978-90-77381-57-1

ISSN

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e-ISSN

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Počet stran výsledku

3

Strana od-do

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Název nakladatele

EUROSIS - ETI

Místo vydání

Ghent

Místo konání akce

Hasselt

Datum konání akce

25. 10. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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