A real-time spike-timing classifier of spatio-temporal patterns

Considering the problem of recognizing non-verbal cues in Human–Robot Interaction applications, this paper proposes a novel real-time unsupervised spike timing neural network for recognition and early detection of spatio-temporal human gestures. Two spiking network classifiers one based on Izhikevic...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Rekabdar, Banafsheh [verfasserIn] Fraser, Luke [verfasserIn] Nicolescu, Monica [verfasserIn] Nicolescu, Mircea [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Spike timing neural network STDP Real time classifier Izhikevich spiking neuron model Integrate-and-Fire-or-Burst neuron model HRI CUDA

Übergeordnetes Werk:	Enthalten in: Neurocomputing - Amsterdam : Elsevier, 1989, 311, Seite 183-196
Übergeordnetes Werk:	volume:311 ; pages:183-196

DOI / URN:	10.1016/j.neucom.2018.05.069

Katalog-ID:	ELV003515710

Internformat


LEADER	01000caa a22002652 4500
001	ELV003515710
003	DE-627
005	20230524130458.0
007	cr uuu---uuuuu
008	230430s2018 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.neucom.2018.05.069 \|2 doi
035			\|a (DE-627)ELV003515710
035			\|a (ELSEVIER)S0925-2312(18)30654-4
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 610 \|q DE-600
084			\|a 54.72 \|2 bkl
100	1		\|a Rekabdar, Banafsheh \|e verfasserin \|4 aut
245	1	0	\|a A real-time spike-timing classifier of spatio-temporal patterns
264		1	\|c 2018
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Considering the problem of recognizing non-verbal cues in Human–Robot Interaction applications, this paper proposes a novel real-time unsupervised spike timing neural network for recognition and early detection of spatio-temporal human gestures. Two spiking network classifiers one based on Izhikevich neuron model, and the other one based on Integrate-and-Fire-or-Burst neuron model have been implemented in CUDA, and allow the classification to be performed in real-time. To evaluate the performance of this proposal, we test the case of a physical robot observing air-handwritings of human gesture. The proposed approaches run in real-time, thus they are suitable for human–robot applications; they allow real-time early classifying human gestures and actions while they require a very small number of training samples. In comparing to other prominent techniques, our approaches demonstrate superior accuracy and are suitable for early classification of different types of human actions in time-sensitive mobile applications such as robotics.
650		4	\|a Spike timing neural network
650		4	\|a STDP
650		4	\|a Real time classifier
650		4	\|a Izhikevich spiking neuron model
650		4	\|a Integrate-and-Fire-or-Burst neuron model
650		4	\|a HRI
650		4	\|a CUDA
700	1		\|a Fraser, Luke \|e verfasserin \|4 aut
700	1		\|a Nicolescu, Monica \|e verfasserin \|4 aut
700	1		\|a Nicolescu, Mircea \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Neurocomputing \|d Amsterdam : Elsevier, 1989 \|g 311, Seite 183-196 \|h Online-Ressource \|w (DE-627)271176008 \|w (DE-600)1479006-3 \|w (DE-576)078412358 \|x 1872-8286 \|7 nnns
773	1	8	\|g volume:311 \|g pages:183-196
912			\|a GBV_USEFLAG_U
912			\|a SYSFLAG_U
912			\|a GBV_ELV
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 54.72 \|j Künstliche Intelligenz
951			\|a AR
952			\|d 311 \|h 183-196

Indexfelder

author_variant	b r br l f lf m n mn m n mn
matchkey_str	article:18728286:2018----::ratmsieiiglsiirfpto
hierarchy_sort_str	2018
bklnumber	54.72
publishDate	2018
allfields	10.1016/j.neucom.2018.05.069 doi (DE-627)ELV003515710 (ELSEVIER)S0925-2312(18)30654-4 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Rekabdar, Banafsheh verfasserin aut A real-time spike-timing classifier of spatio-temporal patterns 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Considering the problem of recognizing non-verbal cues in Human–Robot Interaction applications, this paper proposes a novel real-time unsupervised spike timing neural network for recognition and early detection of spatio-temporal human gestures. Two spiking network classifiers one based on Izhikevich neuron model, and the other one based on Integrate-and-Fire-or-Burst neuron model have been implemented in CUDA, and allow the classification to be performed in real-time. To evaluate the performance of this proposal, we test the case of a physical robot observing air-handwritings of human gesture. The proposed approaches run in real-time, thus they are suitable for human–robot applications; they allow real-time early classifying human gestures and actions while they require a very small number of training samples. In comparing to other prominent techniques, our approaches demonstrate superior accuracy and are suitable for early classification of different types of human actions in time-sensitive mobile applications such as robotics. Spike timing neural network STDP Real time classifier Izhikevich spiking neuron model Integrate-and-Fire-or-Burst neuron model HRI CUDA Fraser, Luke verfasserin aut Nicolescu, Monica verfasserin aut Nicolescu, Mircea verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 311, Seite 183-196 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:311 pages:183-196 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 311 183-196
spelling	10.1016/j.neucom.2018.05.069 doi (DE-627)ELV003515710 (ELSEVIER)S0925-2312(18)30654-4 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Rekabdar, Banafsheh verfasserin aut A real-time spike-timing classifier of spatio-temporal patterns 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Considering the problem of recognizing non-verbal cues in Human–Robot Interaction applications, this paper proposes a novel real-time unsupervised spike timing neural network for recognition and early detection of spatio-temporal human gestures. Two spiking network classifiers one based on Izhikevich neuron model, and the other one based on Integrate-and-Fire-or-Burst neuron model have been implemented in CUDA, and allow the classification to be performed in real-time. To evaluate the performance of this proposal, we test the case of a physical robot observing air-handwritings of human gesture. The proposed approaches run in real-time, thus they are suitable for human–robot applications; they allow real-time early classifying human gestures and actions while they require a very small number of training samples. In comparing to other prominent techniques, our approaches demonstrate superior accuracy and are suitable for early classification of different types of human actions in time-sensitive mobile applications such as robotics. Spike timing neural network STDP Real time classifier Izhikevich spiking neuron model Integrate-and-Fire-or-Burst neuron model HRI CUDA Fraser, Luke verfasserin aut Nicolescu, Monica verfasserin aut Nicolescu, Mircea verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 311, Seite 183-196 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:311 pages:183-196 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 311 183-196
allfields_unstemmed	10.1016/j.neucom.2018.05.069 doi (DE-627)ELV003515710 (ELSEVIER)S0925-2312(18)30654-4 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Rekabdar, Banafsheh verfasserin aut A real-time spike-timing classifier of spatio-temporal patterns 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Considering the problem of recognizing non-verbal cues in Human–Robot Interaction applications, this paper proposes a novel real-time unsupervised spike timing neural network for recognition and early detection of spatio-temporal human gestures. Two spiking network classifiers one based on Izhikevich neuron model, and the other one based on Integrate-and-Fire-or-Burst neuron model have been implemented in CUDA, and allow the classification to be performed in real-time. To evaluate the performance of this proposal, we test the case of a physical robot observing air-handwritings of human gesture. The proposed approaches run in real-time, thus they are suitable for human–robot applications; they allow real-time early classifying human gestures and actions while they require a very small number of training samples. In comparing to other prominent techniques, our approaches demonstrate superior accuracy and are suitable for early classification of different types of human actions in time-sensitive mobile applications such as robotics. Spike timing neural network STDP Real time classifier Izhikevich spiking neuron model Integrate-and-Fire-or-Burst neuron model HRI CUDA Fraser, Luke verfasserin aut Nicolescu, Monica verfasserin aut Nicolescu, Mircea verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 311, Seite 183-196 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:311 pages:183-196 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 311 183-196
allfieldsGer	10.1016/j.neucom.2018.05.069 doi (DE-627)ELV003515710 (ELSEVIER)S0925-2312(18)30654-4 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Rekabdar, Banafsheh verfasserin aut A real-time spike-timing classifier of spatio-temporal patterns 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Considering the problem of recognizing non-verbal cues in Human–Robot Interaction applications, this paper proposes a novel real-time unsupervised spike timing neural network for recognition and early detection of spatio-temporal human gestures. Two spiking network classifiers one based on Izhikevich neuron model, and the other one based on Integrate-and-Fire-or-Burst neuron model have been implemented in CUDA, and allow the classification to be performed in real-time. To evaluate the performance of this proposal, we test the case of a physical robot observing air-handwritings of human gesture. The proposed approaches run in real-time, thus they are suitable for human–robot applications; they allow real-time early classifying human gestures and actions while they require a very small number of training samples. In comparing to other prominent techniques, our approaches demonstrate superior accuracy and are suitable for early classification of different types of human actions in time-sensitive mobile applications such as robotics. Spike timing neural network STDP Real time classifier Izhikevich spiking neuron model Integrate-and-Fire-or-Burst neuron model HRI CUDA Fraser, Luke verfasserin aut Nicolescu, Monica verfasserin aut Nicolescu, Mircea verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 311, Seite 183-196 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:311 pages:183-196 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 311 183-196
allfieldsSound	10.1016/j.neucom.2018.05.069 doi (DE-627)ELV003515710 (ELSEVIER)S0925-2312(18)30654-4 DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Rekabdar, Banafsheh verfasserin aut A real-time spike-timing classifier of spatio-temporal patterns 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Considering the problem of recognizing non-verbal cues in Human–Robot Interaction applications, this paper proposes a novel real-time unsupervised spike timing neural network for recognition and early detection of spatio-temporal human gestures. Two spiking network classifiers one based on Izhikevich neuron model, and the other one based on Integrate-and-Fire-or-Burst neuron model have been implemented in CUDA, and allow the classification to be performed in real-time. To evaluate the performance of this proposal, we test the case of a physical robot observing air-handwritings of human gesture. The proposed approaches run in real-time, thus they are suitable for human–robot applications; they allow real-time early classifying human gestures and actions while they require a very small number of training samples. In comparing to other prominent techniques, our approaches demonstrate superior accuracy and are suitable for early classification of different types of human actions in time-sensitive mobile applications such as robotics. Spike timing neural network STDP Real time classifier Izhikevich spiking neuron model Integrate-and-Fire-or-Burst neuron model HRI CUDA Fraser, Luke verfasserin aut Nicolescu, Monica verfasserin aut Nicolescu, Mircea verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 311, Seite 183-196 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:311 pages:183-196 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 311 183-196
language	English
source	Enthalten in Neurocomputing 311, Seite 183-196 volume:311 pages:183-196
sourceStr	Enthalten in Neurocomputing 311, Seite 183-196 volume:311 pages:183-196
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Spike timing neural network STDP Real time classifier Izhikevich spiking neuron model Integrate-and-Fire-or-Burst neuron model HRI CUDA
dewey-raw	610
isfreeaccess_bool	false
container_title	Neurocomputing
authorswithroles_txt_mv	Rekabdar, Banafsheh @@aut@@ Fraser, Luke @@aut@@ Nicolescu, Monica @@aut@@ Nicolescu, Mircea @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	271176008
dewey-sort	3610
id	ELV003515710
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV003515710</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524130458.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230430s2018 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neucom.2018.05.069</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV003515710</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0925-2312(18)30654-4</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rekabdar, Banafsheh</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A real-time spike-timing classifier of spatio-temporal patterns</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Considering the problem of recognizing non-verbal cues in Human–Robot Interaction applications, this paper proposes a novel real-time unsupervised spike timing neural network for recognition and early detection of spatio-temporal human gestures. Two spiking network classifiers one based on Izhikevich neuron model, and the other one based on Integrate-and-Fire-or-Burst neuron model have been implemented in CUDA, and allow the classification to be performed in real-time. To evaluate the performance of this proposal, we test the case of a physical robot observing air-handwritings of human gesture. The proposed approaches run in real-time, thus they are suitable for human–robot applications; they allow real-time early classifying human gestures and actions while they require a very small number of training samples. In comparing to other prominent techniques, our approaches demonstrate superior accuracy and are suitable for early classification of different types of human actions in time-sensitive mobile applications such as robotics.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spike timing neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">STDP</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Real time classifier</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Izhikevich spiking neuron model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Integrate-and-Fire-or-Burst neuron model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">HRI</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CUDA</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fraser, Luke</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nicolescu, Monica</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nicolescu, Mircea</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neurocomputing</subfield><subfield code="d">Amsterdam : Elsevier, 1989</subfield><subfield code="g">311, Seite 183-196</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)271176008</subfield><subfield code="w">(DE-600)1479006-3</subfield><subfield code="w">(DE-576)078412358</subfield><subfield code="x">1872-8286</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:311</subfield><subfield code="g">pages:183-196</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72</subfield><subfield code="j">Künstliche Intelligenz</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">311</subfield><subfield code="h">183-196</subfield></datafield></record></collection>
author	Rekabdar, Banafsheh
spellingShingle	Rekabdar, Banafsheh ddc 610 bkl 54.72 misc Spike timing neural network misc STDP misc Real time classifier misc Izhikevich spiking neuron model misc Integrate-and-Fire-or-Burst neuron model misc HRI misc CUDA A real-time spike-timing classifier of spatio-temporal patterns
authorStr	Rekabdar, Banafsheh
ppnlink_with_tag_str_mv	@@773@@(DE-627)271176008
format	electronic Article
dewey-ones	610 - Medicine & health
delete_txt_mv	keep
author_role	aut aut aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
issn	1872-8286
topic_title	610 DE-600 54.72 bkl A real-time spike-timing classifier of spatio-temporal patterns Spike timing neural network STDP Real time classifier Izhikevich spiking neuron model Integrate-and-Fire-or-Burst neuron model HRI CUDA
topic	ddc 610 bkl 54.72 misc Spike timing neural network misc STDP misc Real time classifier misc Izhikevich spiking neuron model misc Integrate-and-Fire-or-Burst neuron model misc HRI misc CUDA
topic_unstemmed	ddc 610 bkl 54.72 misc Spike timing neural network misc STDP misc Real time classifier misc Izhikevich spiking neuron model misc Integrate-and-Fire-or-Burst neuron model misc HRI misc CUDA
topic_browse	ddc 610 bkl 54.72 misc Spike timing neural network misc STDP misc Real time classifier misc Izhikevich spiking neuron model misc Integrate-and-Fire-or-Burst neuron model misc HRI misc CUDA
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Neurocomputing
hierarchy_parent_id	271176008
dewey-tens	610 - Medicine & health
hierarchy_top_title	Neurocomputing
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358
title	A real-time spike-timing classifier of spatio-temporal patterns
ctrlnum	(DE-627)ELV003515710 (ELSEVIER)S0925-2312(18)30654-4
title_full	A real-time spike-timing classifier of spatio-temporal patterns
author_sort	Rekabdar, Banafsheh
journal	Neurocomputing
journalStr	Neurocomputing
lang_code	eng
isOA_bool	false
dewey-hundreds	600 - Technology
recordtype	marc
publishDateSort	2018
contenttype_str_mv	zzz
container_start_page	183
author_browse	Rekabdar, Banafsheh Fraser, Luke Nicolescu, Monica Nicolescu, Mircea
container_volume	311
class	610 DE-600 54.72 bkl
format_se	Elektronische Aufsätze
author-letter	Rekabdar, Banafsheh
doi_str_mv	10.1016/j.neucom.2018.05.069
dewey-full	610
author2-role	verfasserin
title_sort	a real-time spike-timing classifier of spatio-temporal patterns
title_auth	A real-time spike-timing classifier of spatio-temporal patterns
abstract	Considering the problem of recognizing non-verbal cues in Human–Robot Interaction applications, this paper proposes a novel real-time unsupervised spike timing neural network for recognition and early detection of spatio-temporal human gestures. Two spiking network classifiers one based on Izhikevich neuron model, and the other one based on Integrate-and-Fire-or-Burst neuron model have been implemented in CUDA, and allow the classification to be performed in real-time. To evaluate the performance of this proposal, we test the case of a physical robot observing air-handwritings of human gesture. The proposed approaches run in real-time, thus they are suitable for human–robot applications; they allow real-time early classifying human gestures and actions while they require a very small number of training samples. In comparing to other prominent techniques, our approaches demonstrate superior accuracy and are suitable for early classification of different types of human actions in time-sensitive mobile applications such as robotics.
abstractGer	Considering the problem of recognizing non-verbal cues in Human–Robot Interaction applications, this paper proposes a novel real-time unsupervised spike timing neural network for recognition and early detection of spatio-temporal human gestures. Two spiking network classifiers one based on Izhikevich neuron model, and the other one based on Integrate-and-Fire-or-Burst neuron model have been implemented in CUDA, and allow the classification to be performed in real-time. To evaluate the performance of this proposal, we test the case of a physical robot observing air-handwritings of human gesture. The proposed approaches run in real-time, thus they are suitable for human–robot applications; they allow real-time early classifying human gestures and actions while they require a very small number of training samples. In comparing to other prominent techniques, our approaches demonstrate superior accuracy and are suitable for early classification of different types of human actions in time-sensitive mobile applications such as robotics.
abstract_unstemmed	Considering the problem of recognizing non-verbal cues in Human–Robot Interaction applications, this paper proposes a novel real-time unsupervised spike timing neural network for recognition and early detection of spatio-temporal human gestures. Two spiking network classifiers one based on Izhikevich neuron model, and the other one based on Integrate-and-Fire-or-Burst neuron model have been implemented in CUDA, and allow the classification to be performed in real-time. To evaluate the performance of this proposal, we test the case of a physical robot observing air-handwritings of human gesture. The proposed approaches run in real-time, thus they are suitable for human–robot applications; they allow real-time early classifying human gestures and actions while they require a very small number of training samples. In comparing to other prominent techniques, our approaches demonstrate superior accuracy and are suitable for early classification of different types of human actions in time-sensitive mobile applications such as robotics.
collection_details	GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393
title_short	A real-time spike-timing classifier of spatio-temporal patterns
remote_bool	true
author2	Fraser, Luke Nicolescu, Monica Nicolescu, Mircea
author2Str	Fraser, Luke Nicolescu, Monica Nicolescu, Mircea
ppnlink	271176008
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.neucom.2018.05.069
up_date	2024-07-06T19:53:04.459Z
_version_	1803860662771777536
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV003515710</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524130458.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230430s2018 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neucom.2018.05.069</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV003515710</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0925-2312(18)30654-4</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rekabdar, Banafsheh</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A real-time spike-timing classifier of spatio-temporal patterns</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Considering the problem of recognizing non-verbal cues in Human–Robot Interaction applications, this paper proposes a novel real-time unsupervised spike timing neural network for recognition and early detection of spatio-temporal human gestures. Two spiking network classifiers one based on Izhikevich neuron model, and the other one based on Integrate-and-Fire-or-Burst neuron model have been implemented in CUDA, and allow the classification to be performed in real-time. To evaluate the performance of this proposal, we test the case of a physical robot observing air-handwritings of human gesture. The proposed approaches run in real-time, thus they are suitable for human–robot applications; they allow real-time early classifying human gestures and actions while they require a very small number of training samples. In comparing to other prominent techniques, our approaches demonstrate superior accuracy and are suitable for early classification of different types of human actions in time-sensitive mobile applications such as robotics.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spike timing neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">STDP</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Real time classifier</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Izhikevich spiking neuron model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Integrate-and-Fire-or-Burst neuron model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">HRI</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CUDA</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fraser, Luke</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nicolescu, Monica</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nicolescu, Mircea</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neurocomputing</subfield><subfield code="d">Amsterdam : Elsevier, 1989</subfield><subfield code="g">311, Seite 183-196</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)271176008</subfield><subfield code="w">(DE-600)1479006-3</subfield><subfield code="w">(DE-576)078412358</subfield><subfield code="x">1872-8286</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:311</subfield><subfield code="g">pages:183-196</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72</subfield><subfield code="j">Künstliche Intelligenz</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">311</subfield><subfield code="h">183-196</subfield></datafield></record></collection>
score	7.3974905

Nicht das Richtige dabei?

Schreiben Sie uns!

A real-time spike-timing classifier of spatio-temporal patterns

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?