HapticV1, PascalFrancis, Corpus, bibRecord, 000850

2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism

Identifieur interne : 000850 ( PascalFrancis/Corpus ); précédent : 000849; suivant : 000851

2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism

Auteurs : QINGXIANG WU ; Thomas Martin Mcginnity ; Liam Maguire ; Ammar Belatreche ; Brendan Glackin

Source :

Neural networks [ 0893-6080 ] ; 2008.

RBID : Pascal:09-0026168

Descripteurs français

Pascal (Inist)
- Changement coordonnée, Plasticité synaptique, Apprentissage, Cerveau, Encéphale, Homme, Système nerveux central, Représentation spatiale, Vision, Sensibilité tactile, Rétine, Corps, Neurone impulsionnel, Modélisation, Coordonnée polaire, Image virtuelle, Voie visuelle, Synapse, Imagerie visuelle, Réseau neuronal, Intelligence artificielle, ..

English descriptors

KwdEn :
- Artificial intelligence, Body, Brain, Central nervous system, Encephalon, Human, Learning, Modeling, Neural network, Polar coordinate, Retina, Spatial representation, Spiking neuron, Synapse, Synaptic plasticity, Tactile sensitivity, Transformation of coordinates, Virtual image, Vision, Visual imagery, Visual pathway.

Abstract

In order to plan accurate motor actions, the brain needs to build an integrated spatial representation associated with visual stimuli and haptic stimuli. Since visual stimuli are represented in retina-centered co-ordinates and haptic stimuli are represented in body-centered co-ordinates, co-ordinate transformations must occur between the retina-centered co-ordinates and body-centered co-ordinates. A spiking neural network (SNN) model, which is trained with spike-timing-dependent-plasticity (STDP), is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation, to create a virtual image map of a haptic input. Through the visual pathway, a position signal corresponding to the haptic input is used to train the SNN with STDP synapses such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. The model can be applied to explain co-ordinate transformation in spiking neuron based systems. The principle can be used in artificial intelligent systems to process complex co-ordinate transformations represented by biological stimuli.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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Format Inist (serveur)

NO :	PASCAL 09-0026168 INIST
ET :	2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism
AU :	QINGXIANG WU; MCGINNITY (Thomas Martin); MAGUIRE (Liam); BELATRECHE (Ammar); GLACKIN (Brendan)
AF :	School of Computing and Intelligent Systems, University of Ulster, Magee Campus/Derry, BT48 7JL, N.Ireland/Royaume-Uni (1 aut., 2 aut., 3 aut., 4 aut., 5 aut.)
DT :	Publication en série; Niveau analytique
SO :	Neural networks; ISSN 0893-6080; Royaume-Uni; Da. 2008; Vol. 21; No. 9; Pp. 1318-1327; Bibl. 3/4 p.
LA :	Anglais
EA :	In order to plan accurate motor actions, the brain needs to build an integrated spatial representation associated with visual stimuli and haptic stimuli. Since visual stimuli are represented in retina-centered co-ordinates and haptic stimuli are represented in body-centered co-ordinates, co-ordinate transformations must occur between the retina-centered co-ordinates and body-centered co-ordinates. A spiking neural network (SNN) model, which is trained with spike-timing-dependent-plasticity (STDP), is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation, to create a virtual image map of a haptic input. Through the visual pathway, a position signal corresponding to the haptic input is used to train the SNN with STDP synapses such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. The model can be applied to explain co-ordinate transformation in spiking neuron based systems. The principle can be used in artificial intelligent systems to process complex co-ordinate transformations represented by biological stimuli.
CC :	001D02C06; 002A26E03; 002A26E05; 001D02C02
FD :	Changement coordonnée; Plasticité synaptique; Apprentissage; Cerveau; Encéphale; Homme; Système nerveux central; Représentation spatiale; Vision; Sensibilité tactile; Rétine; Corps; Neurone impulsionnel; Modélisation; Coordonnée polaire; Image virtuelle; Voie visuelle; Synapse; Imagerie visuelle; Réseau neuronal; Intelligence artificielle; .
ED :	Transformation of coordinates; Synaptic plasticity; Learning; Brain; Encephalon; Human; Central nervous system; Spatial representation; Vision; Tactile sensitivity; Retina; Body; Spiking neuron; Modeling; Polar coordinate; Virtual image; Visual pathway; Synapse; Visual imagery; Neural network; Artificial intelligence
SD :	Cambio coordenadas; Plasticidad sináptica; Aprendizaje; Cerebro; Encéfalo; Hombre; Sistema nervioso central; Representación espacial; Visión; Sensibilidad tactil; Retina; Cuerpo; Neurona pulsante; Modelización; Coordenada polar; Imagen virtual; Vía visual; Sinapsis; Imaginería visual; Red neuronal; Inteligencia artificial
LO :	INIST-21689.354000184411220130
ID :	09-0026168

Links to Exploration step

Pascal:09-0026168

Le document en format XML

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<front><div type="abstract" xml:lang="en">In order to plan accurate motor actions, the brain needs to build an integrated spatial representation associated with visual stimuli and haptic stimuli. Since visual stimuli are represented in retina-centered co-ordinates and haptic stimuli are represented in body-centered co-ordinates, co-ordinate transformations must occur between the retina-centered co-ordinates and body-centered co-ordinates. A spiking neural network (SNN) model, which is trained with spike-timing-dependent-plasticity (STDP), is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation, to create a virtual image map of a haptic input. Through the visual pathway, a position signal corresponding to the haptic input is used to train the SNN with STDP synapses such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. The model can be applied to explain co-ordinate transformation in spiking neuron based systems. The principle can be used in artificial intelligent systems to process complex co-ordinate transformations represented by biological stimuli.</div>
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<fA45><s0>3/4 p.</s0>
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<fA47 i1="01" i2="1"><s0>09-0026168</s0>
</fA47>
<fA60><s1>P</s1>
</fA60>
<fA61><s0>A</s0>
</fA61>
<fA64 i1="01" i2="1"><s0>Neural networks</s0>
</fA64>
<fA66 i1="01"><s0>GBR</s0>
</fA66>
<fC01 i1="01" l="ENG"><s0>In order to plan accurate motor actions, the brain needs to build an integrated spatial representation associated with visual stimuli and haptic stimuli. Since visual stimuli are represented in retina-centered co-ordinates and haptic stimuli are represented in body-centered co-ordinates, co-ordinate transformations must occur between the retina-centered co-ordinates and body-centered co-ordinates. A spiking neural network (SNN) model, which is trained with spike-timing-dependent-plasticity (STDP), is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation, to create a virtual image map of a haptic input. Through the visual pathway, a position signal corresponding to the haptic input is used to train the SNN with STDP synapses such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. The model can be applied to explain co-ordinate transformation in spiking neuron based systems. The principle can be used in artificial intelligent systems to process complex co-ordinate transformations represented by biological stimuli.</s0>
</fC01>
<fC02 i1="01" i2="X"><s0>001D02C06</s0>
</fC02>
<fC02 i1="02" i2="X"><s0>002A26E03</s0>
</fC02>
<fC02 i1="03" i2="X"><s0>002A26E05</s0>
</fC02>
<fC02 i1="04" i2="X"><s0>001D02C02</s0>
</fC02>
<fC03 i1="01" i2="X" l="FRE"><s0>Changement coordonnée</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="ENG"><s0>Transformation of coordinates</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="SPA"><s0>Cambio coordenadas</s0>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="X" l="FRE"><s0>Plasticité synaptique</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG"><s0>Synaptic plasticity</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA"><s0>Plasticidad sináptica</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE"><s0>Apprentissage</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG"><s0>Learning</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA"><s0>Aprendizaje</s0>
<s5>03</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Cerveau</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Brain</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Cerebro</s0>
<s5>04</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Encéphale</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Encephalon</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Encéfalo</s0>
<s5>05</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Homme</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Human</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Hombre</s0>
<s5>06</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Système nerveux central</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Central nervous system</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Sistema nervioso central</s0>
<s5>07</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Représentation spatiale</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Spatial representation</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Representación espacial</s0>
<s5>08</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Vision</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Vision</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Visión</s0>
<s5>09</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Sensibilité tactile</s0>
<s5>10</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Tactile sensitivity</s0>
<s5>10</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Sensibilidad tactil</s0>
<s5>10</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Rétine</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Retina</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Retina</s0>
<s5>11</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Corps</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Body</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Cuerpo</s0>
<s5>12</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Neurone impulsionnel</s0>
<s5>13</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Spiking neuron</s0>
<s5>13</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Neurona pulsante</s0>
<s5>13</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Modélisation</s0>
<s5>14</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Modeling</s0>
<s5>14</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Modelización</s0>
<s5>14</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Coordonnée polaire</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Polar coordinate</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Coordenada polar</s0>
<s5>15</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Image virtuelle</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Virtual image</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Imagen virtual</s0>
<s5>16</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Voie visuelle</s0>
<s5>17</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Visual pathway</s0>
<s5>17</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Vía visual</s0>
<s5>17</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Synapse</s0>
<s5>18</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Synapse</s0>
<s5>18</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Sinapsis</s0>
<s5>18</s5>
</fC03>
<fC03 i1="19" i2="X" l="FRE"><s0>Imagerie visuelle</s0>
<s5>19</s5>
</fC03>
<fC03 i1="19" i2="X" l="ENG"><s0>Visual imagery</s0>
<s5>19</s5>
</fC03>
<fC03 i1="19" i2="X" l="SPA"><s0>Imaginería visual</s0>
<s5>19</s5>
</fC03>
<fC03 i1="20" i2="X" l="FRE"><s0>Réseau neuronal</s0>
<s5>20</s5>
</fC03>
<fC03 i1="20" i2="X" l="ENG"><s0>Neural network</s0>
<s5>20</s5>
</fC03>
<fC03 i1="20" i2="X" l="SPA"><s0>Red neuronal</s0>
<s5>20</s5>
</fC03>
<fC03 i1="21" i2="X" l="FRE"><s0>Intelligence artificielle</s0>
<s5>21</s5>
</fC03>
<fC03 i1="21" i2="X" l="ENG"><s0>Artificial intelligence</s0>
<s5>21</s5>
</fC03>
<fC03 i1="21" i2="X" l="SPA"><s0>Inteligencia artificial</s0>
<s5>21</s5>
</fC03>
<fC03 i1="22" i2="X" l="FRE"><s0>.</s0>
<s4>INC</s4>
<s5>82</s5>
</fC03>
<fN21><s1>012</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
<server><NO>PASCAL 09-0026168 INIST</NO>
<ET>2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism</ET>
<AU>QINGXIANG WU; MCGINNITY (Thomas Martin); MAGUIRE (Liam); BELATRECHE (Ammar); GLACKIN (Brendan)</AU>
<AF>School of Computing and Intelligent Systems, University of Ulster, Magee Campus/Derry, BT48 7JL, N.Ireland/Royaume-Uni (1 aut., 2 aut., 3 aut., 4 aut., 5 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Neural networks; ISSN 0893-6080; Royaume-Uni; Da. 2008; Vol. 21; No. 9; Pp. 1318-1327; Bibl. 3/4 p.</SO>
<LA>Anglais</LA>
<EA>In order to plan accurate motor actions, the brain needs to build an integrated spatial representation associated with visual stimuli and haptic stimuli. Since visual stimuli are represented in retina-centered co-ordinates and haptic stimuli are represented in body-centered co-ordinates, co-ordinate transformations must occur between the retina-centered co-ordinates and body-centered co-ordinates. A spiking neural network (SNN) model, which is trained with spike-timing-dependent-plasticity (STDP), is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation, to create a virtual image map of a haptic input. Through the visual pathway, a position signal corresponding to the haptic input is used to train the SNN with STDP synapses such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. The model can be applied to explain co-ordinate transformation in spiking neuron based systems. The principle can be used in artificial intelligent systems to process complex co-ordinate transformations represented by biological stimuli.</EA>
<CC>001D02C06; 002A26E03; 002A26E05; 001D02C02</CC>
<FD>Changement coordonnée; Plasticité synaptique; Apprentissage; Cerveau; Encéphale; Homme; Système nerveux central; Représentation spatiale; Vision; Sensibilité tactile; Rétine; Corps; Neurone impulsionnel; Modélisation; Coordonnée polaire; Image virtuelle; Voie visuelle; Synapse; Imagerie visuelle; Réseau neuronal; Intelligence artificielle; .</FD>
<ED>Transformation of coordinates; Synaptic plasticity; Learning; Brain; Encephalon; Human; Central nervous system; Spatial representation; Vision; Tactile sensitivity; Retina; Body; Spiking neuron; Modeling; Polar coordinate; Virtual image; Visual pathway; Synapse; Visual imagery; Neural network; Artificial intelligence</ED>
<SD>Cambio coordenadas; Plasticidad sináptica; Aprendizaje; Cerebro; Encéfalo; Hombre; Sistema nervioso central; Representación espacial; Visión; Sensibilidad tactil; Retina; Cuerpo; Neurona pulsante; Modelización; Coordenada polar; Imagen virtual; Vía visual; Sinapsis; Imaginería visual; Red neuronal; Inteligencia artificial</SD>
<LO>INIST-21689.354000184411220130</LO>
<ID>09-0026168</ID>
</server>
</inist>
</record>

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2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism

2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism

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