Foundation of Notation and Classification of Nonconventional Static and Dynamic Neural Units
Result 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 simplifiedmathematical 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.
Keywords
adaptationclassificationnonconventional artificial neural unitnonlinear aggregating functionnotationsynaptic junctiontime delay
The result's identifiers
Result code in IS VaVaI
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Foundation of Notation and Classification of Nonconventional Static and Dynamic Neural Units
Original language 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 simplifiedmathematical 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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BC - Theory and management systems
OECD FORD branch
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Result continuities
Project
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2007
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Cognitive Informatics, 6th IEEE International Conference on
ISBN
978-1-4244-1327-0
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
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Publisher name
IEEE CS Press
Place of publication
California
Event location
Lake Tahoe, CA
Event date
Aug 6, 2007
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000250542300051
Basic information
Result type
D - Article in proceedings
CEP
BC - Theory and management systems
Year of implementation
2007