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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F08%3A00145486%21RIV10-MSM-21230___
rdf:type
skos:Concept n12:Vysledek
dcterms:description
In this paper we present a new self-organizing neural network called Temporal Hebbian Self-organizing Map (THSOM) suitable for modelling of temporal sequences. The network is based on Kohonen's Self-organizing Map, which is extended with a layer of full recurrent connections among the neurons. The layer of recurrent connections is trained with Hebb's rule. The recurrent layer represents temporal order of the input vectors. The THSOM brings a straightforward way of embedding context information in recurrent SOM using neurons with Euclidean metric and scalar product. The recurrent layer can be easily converted into a stochastic automaton (Markov Chain) generating sequences used for previous THSOM training. Finally, two real world examples of THSOM usage are presented. THSOM was applied to extraction of road network from GPS data and to construction of spatio-temporal models of spike train sequences measured in human brain in vivo. In this paper we present a new self-organizing neural network called Temporal Hebbian Self-organizing Map (THSOM) suitable for modelling of temporal sequences. The network is based on Kohonen's Self-organizing Map, which is extended with a layer of full recurrent connections among the neurons. The layer of recurrent connections is trained with Hebb's rule. The recurrent layer represents temporal order of the input vectors. The THSOM brings a straightforward way of embedding context information in recurrent SOM using neurons with Euclidean metric and scalar product. The recurrent layer can be easily converted into a stochastic automaton (Markov Chain) generating sequences used for previous THSOM training. Finally, two real world examples of THSOM usage are presented. THSOM was applied to extraction of road network from GPS data and to construction of spatio-temporal models of spike train sequences measured in human brain in vivo.
dcterms:title
Temporal Hebbian Self-Organizing Map for Sequences Temporal Hebbian Self-Organizing Map for Sequences
skos:prefLabel
Temporal Hebbian Self-Organizing Map for Sequences Temporal Hebbian Self-Organizing Map for Sequences
skos:notation
RIV/68407700:21230/08:00145486!RIV10-MSM-21230___
n3:aktivita
n19:Z
n3:aktivity
Z(MSM6840770012)
n3:dodaniDat
n4:2010
n3:domaciTvurceVysledku
n8:7438907 n8:7035586
n3:druhVysledku
n10:D
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n21:predkladatel
n3:idSjednocenehoVysledku
399407
n3:idVysledku
RIV/68407700:21230/08:00145486
n3:jazykVysledku
n11:eng
n3:klicovaSlova
artificial neural networks; recurrent neural networks; self-organization; self-organizing maps; temporals sequences processing
n3:klicoveSlovo
n9:self-organization n9:artificial%20neural%20networks n9:temporals%20sequences%20processing n9:self-organizing%20maps n9:recurrent%20neural%20networks
n3:kontrolniKodProRIV
[1D711BB643D0]
n3:mistoKonaniAkce
Prague
n3:mistoVydani
Heidelberg
n3:nazevZdroje
Artificial Neural Networks - ICANN 2008, PT I
n3:obor
n6:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n4:2008
n3:tvurceVysledku
Koutník, Jan Šnorek, Miroslav
n3:typAkce
n13:EUR
n3:wos
000259566200065
n3:zahajeniAkce
2008-09-03+02:00
n3:zamer
n15:MSM6840770012
s:issn
0302-9743
s:numberOfPages
10
n20:hasPublisher
Springer-Verlag
n17:isbn
978-3-540-87535-2
n18:organizacniJednotka
21230