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Statements

Subject Item
n2:RIV%2F68407700%3A21220%2F07%3A00133313%21RIV11-MSM-21220___
rdf:type
n6:Vysledek skos:Concept
dcterms:description
The paper introduces basic types of nonconventional artificial neural units, their notation and classification. The notation and classification of dynamic higher-order nonlinear neural units, time-delay dynamic neural units, and time-delay higher-order nonlinear neural units is introduced. Introduction into the simplified parallel of higher-order nonlinear aggregating function of artificial nonconventional neural units and synaptic and somatic operation of biological neurons is made. Based on simplified mathematical notation, it is proposed that nonlinear aggregating function of neural inputs should be understood as composition of synaptic and partial somatic neural operation also for static neural units. It unravels novel yet universal insight into understanding computationally powerful neurons. The classification of nonconventional artificial neural units is founded according to nonlinearity of aggregating function, the dynamic order, and time-delay implementation in neural units. The paper introduces basic types of nonconventional artificial neural units, their notation and classification. The notation and classification of dynamic higher-order nonlinear neural units, time-delay dynamic neural units, and time-delay higher-order nonlinear neural units is introduced. Introduction into the simplified parallel of higher-order nonlinear aggregating function of artificial nonconventional neural units and synaptic and somatic operation of biological neurons is made. Based on simplified mathematical notation, it is proposed that nonlinear aggregating function of neural inputs should be understood as composition of synaptic and partial somatic neural operation also for static neural units. It unravels novel yet universal insight into understanding computationally powerful neurons. The classification of nonconventional artificial neural units is founded according to nonlinearity of aggregating function, the dynamic order, and time-delay implementation in neural units.
dcterms:title
Foundation of Notation and Classification of Nonconventional Static and Dynamic Neural Units Foundation of Notation and Classification of Nonconventional Static and Dynamic Neural Units
skos:prefLabel
Foundation of Notation and Classification of Nonconventional Static and Dynamic Neural Units Foundation of Notation and Classification of Nonconventional Static and Dynamic Neural Units
skos:notation
RIV/68407700:21220/07:00133313!RIV11-MSM-21220___
n3:aktivita
n8:P
n3:aktivity
P(2B06023)
n3:dodaniDat
n9:2011
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n13:8200599 n13:2339994
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n11:D
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
422453
n3:idVysledku
RIV/68407700:21220/07:00133313
n3:jazykVysledku
n10:eng
n3:klicovaSlova
adaptation; classification; nonconventional artificial neural unit; nonlinear aggregating function; notation; synaptic junction; time delay
n3:klicoveSlovo
n4:nonconventional%20artificial%20neural%20unit n4:synaptic%20junction n4:nonlinear%20aggregating%20function n4:time%20delay n4:notation n4:classification n4:adaptation
n3:kontrolniKodProRIV
[C86E5D20E0C4]
n3:mistoKonaniAkce
Lake Tahoe, CA
n3:mistoVydani
California
n3:nazevZdroje
Cognitive Informatics, 6th IEEE International Conference on
n3:obor
n15:BC
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
4
n3:projekt
n21:2B06023
n3:rokUplatneniVysledku
n9:2007
n3:tvurceVysledku
Gupta, M. M. G. Bukovský, Ivo Bíla, Jiří Hou, Z.-G.
n3:typAkce
n17:WRD
n3:wos
000250542300051
n3:zahajeniAkce
2007-08-06+02:00
s:numberOfPages
7
n18:hasPublisher
IEEE CS Press
n7:isbn
978-1-4244-1327-0
n19:organizacniJednotka
21220