Biological context of Hebb learning in artificial neural networks, a review

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F15%3A10283991" target="_blank" >RIV/00216208:11110/15:10283991 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/15:00228895

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.neucom.2014.11.022" target="_blank" >http://dx.doi.org/10.1016/j.neucom.2014.11.022</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neucom.2014.11.022" target="_blank" >10.1016/j.neucom.2014.11.022</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Biological context of Hebb learning in artificial neural networks, a review

Popis výsledku v původním jazyce

In 1949 Donald Olding Hebb formulated a hypothesis describing how neurons excite each other and how the efficiency of this excitation subsequently changes with time. In this paper we present a review of this idea. We evaluate its influences on the development of artificial neural networks and the way we describe biological neural networks. We explain how Hebb's hypothesis fits into the research both of that time and of present. We highlight how it has gone on to inspire many researchers working on artificial neural networks. The underlying biological principles that corroborate this hypothesis, that were discovered much later, are also discussed in addition to recent results in the field and further possible directions of synaptic learning research.

Název v anglickém jazyce

Biological context of Hebb learning in artificial neural networks, a review

Popis výsledku anglicky

In 1949 Donald Olding Hebb formulated a hypothesis describing how neurons excite each other and how the efficiency of this excitation subsequently changes with time. In this paper we present a review of this idea. We evaluate its influences on the development of artificial neural networks and the way we describe biological neural networks. We explain how Hebb's hypothesis fits into the research both of that time and of present. We highlight how it has gone on to inspire many researchers working on artificial neural networks. The underlying biological principles that corroborate this hypothesis, that were discovered much later, are also discussed in addition to recent results in the field and further possible directions of synaptic learning research.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

ED - Fyziologie

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Neurocomputing

ISSN

0925-2312

e-ISSN

—

Svazek periodika

152

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

27-35

Kód UT WoS článku

000349572600004

EID výsledku v databázi Scopus

2-s2.0-84921280899