Adaptive Co-ordinate transformation based on a spike timing-dependent plasticity learning paradigm
Identifieur interne :
000654 ( PascalFrancis/Curation );
précédent :
000653;
suivant :
000655
Adaptive Co-ordinate transformation based on a spike timing-dependent plasticity learning paradigm
Auteurs : QINGXIANG WU [
Royaume-Uni] ;
T. M. Mcginnity [
Royaume-Uni] ;
L. P. Maguire [
Royaume-Uni] ;
A. Belatreche [
Royaume-Uni] ;
B. Glackin [
Royaume-Uni]
Source :
-
Lecture notes in computer science [ 0302-9743 ] ; 2005.
RBID : Pascal:05-0422213
Descripteurs français
- Pascal (Inist)
- Image virtuelle,
Intelligence artificielle,
Système intelligent,
Système temporisé,
Inélasticité,
Plasticité,
Sensibilité tactile,
Système biologique,
Méthode adaptative,
Changement coordonnée,
Réseau neuronal,
..
- Wicri :
English descriptors
- KwdEn :
- Adaptive method,
Artificial intelligence,
Biological system,
Inelasticity,
Intelligent system,
Neural network,
Plasticity,
Tactile sensitivity,
Timed system,
Transformation of coordinates,
Virtual image.
Abstract
A spiking neural network (SNN) model trained with spiking-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 in order to create a virtual image map of a haptic input. The position of the haptic input is used to train the SNN using STDP 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. This principle can be applied to complex co-ordinate transformations in artificial intelligent systems to process biological stimuli.
| pA |
| A01 | 01 | 1 | | @0 0302-9743 |
|---|
| A05 | | | | @2 3610 |
|---|
| A08 | 01 | 1 | ENG | @1 Adaptive Co-ordinate transformation based on a spike timing-dependent plasticity learning paradigm |
|---|
| A09 | 01 | 1 | ENG | @1 Advances in natural computation : Changsha, 27-29 August 2005 |
|---|
| A11 | 01 | 1 | | @1 QINGXIANG WU |
|---|
| A11 | 02 | 1 | | @1 MCGINNITY (T. M.) |
|---|
| A11 | 03 | 1 | | @1 MAGUIRE (L. P.) |
|---|
| A11 | 04 | 1 | | @1 BELATRECHE (A.) |
|---|
| A11 | 05 | 1 | | @1 GLACKIN (B.) |
|---|
| A12 | 01 | 1 | | @1 WANG LIPO @9 ed. |
|---|
| A12 | 02 | 1 | | @1 CHEN KE @9 ed. |
|---|
| A12 | 03 | 1 | | @1 ONG YEW SOON @9 ed. |
|---|
| A14 | 01 | | | @1 School of Computing and Intelligent Systems, University of Ulster, Magee Campus Derry @2 BT48 7JL, N.Ireland @3 GBR @Z 1 aut. @Z 2 aut. @Z 3 aut. @Z 4 aut. @Z 5 aut. |
|---|
| A20 | | | | @2 Part 1, 420-428 |
|---|
| A21 | | | | @1 2005 |
|---|
| A23 | 01 | | | @0 ENG |
|---|
| A26 | 01 | | | @0 3-540-28323-4 |
|---|
| A43 | 01 | | | @1 INIST @2 16343 @5 354000124507310540 |
|---|
| A44 | | | | @0 0000 @1 © 2005 INIST-CNRS. All rights reserved. |
|---|
| A45 | | | | @0 22 ref. |
|---|
| A47 | 01 | 1 | | @0 05-0422213 |
|---|
| A60 | | | | @1 P @2 C |
|---|
| A61 | | | | @0 A |
|---|
| A64 | 01 | 1 | | @0 Lecture notes in computer science |
|---|
| A66 | 01 | | | @0 DEU |
|---|
| C01 | 01 | | ENG | @0 A spiking neural network (SNN) model trained with spiking-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 in order to create a virtual image map of a haptic input. The position of the haptic input is used to train the SNN using STDP 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. This principle can be applied to complex co-ordinate transformations in artificial intelligent systems to process biological stimuli. |
|---|
| C02 | 01 | X | | @0 001D02C |
|---|
| C03 | 01 | X | FRE | @0 Image virtuelle @5 06 |
|---|
| C03 | 01 | X | ENG | @0 Virtual image @5 06 |
|---|
| C03 | 01 | X | SPA | @0 Imagen virtual @5 06 |
|---|
| C03 | 02 | X | FRE | @0 Intelligence artificielle @5 07 |
|---|
| C03 | 02 | X | ENG | @0 Artificial intelligence @5 07 |
|---|
| C03 | 02 | X | SPA | @0 Inteligencia artificial @5 07 |
|---|
| C03 | 03 | X | FRE | @0 Système intelligent @5 08 |
|---|
| C03 | 03 | X | ENG | @0 Intelligent system @5 08 |
|---|
| C03 | 03 | X | SPA | @0 Sistema inteligente @5 08 |
|---|
| C03 | 04 | X | FRE | @0 Système temporisé @5 18 |
|---|
| C03 | 04 | X | ENG | @0 Timed system @5 18 |
|---|
| C03 | 04 | X | SPA | @0 Sistema temporizado @5 18 |
|---|
| C03 | 05 | X | FRE | @0 Inélasticité @5 19 |
|---|
| C03 | 05 | X | ENG | @0 Inelasticity @5 19 |
|---|
| C03 | 05 | X | SPA | @0 Inelasticidad @5 19 |
|---|
| C03 | 06 | X | FRE | @0 Plasticité @5 20 |
|---|
| C03 | 06 | X | ENG | @0 Plasticity @5 20 |
|---|
| C03 | 06 | X | SPA | @0 Plasticidad @5 20 |
|---|
| C03 | 07 | X | FRE | @0 Sensibilité tactile @5 21 |
|---|
| C03 | 07 | X | ENG | @0 Tactile sensitivity @5 21 |
|---|
| C03 | 07 | X | SPA | @0 Sensibilidad tactil @5 21 |
|---|
| C03 | 08 | X | FRE | @0 Système biologique @5 22 |
|---|
| C03 | 08 | X | ENG | @0 Biological system @5 22 |
|---|
| C03 | 08 | X | SPA | @0 Sistema biológico @5 22 |
|---|
| C03 | 09 | X | FRE | @0 Méthode adaptative @5 23 |
|---|
| C03 | 09 | X | ENG | @0 Adaptive method @5 23 |
|---|
| C03 | 09 | X | SPA | @0 Método adaptativo @5 23 |
|---|
| C03 | 10 | X | FRE | @0 Changement coordonnée @5 24 |
|---|
| C03 | 10 | X | ENG | @0 Transformation of coordinates @5 24 |
|---|
| C03 | 10 | X | SPA | @0 Cambio coordenadas @5 24 |
|---|
| C03 | 11 | X | FRE | @0 Réseau neuronal @5 25 |
|---|
| C03 | 11 | X | ENG | @0 Neural network @5 25 |
|---|
| C03 | 11 | X | SPA | @0 Red neuronal @5 25 |
|---|
| C03 | 12 | X | FRE | @0 . @4 INC @5 82 |
|---|
| N21 | | | | @1 290 |
|---|
| N44 | 01 | | | @1 OTO |
|---|
| N82 | | | | @1 OTO |
|---|
|
|---|
| pR |
| A30 | 01 | 1 | ENG | @1 ICNC : international conference on advances in natural computation @2 1 @3 Changsha CHN @4 2005-08-27 |
|---|
|
|---|
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Le document en format XML
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</imprint><seriesStmt><title level="j" type="main">Lecture notes in computer science</title>
<idno type="ISSN">0302-9743</idno>
</seriesStmt><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adaptive method</term>
<term>Artificial intelligence</term>
<term>Biological system</term>
<term>Inelasticity</term>
<term>Intelligent system</term>
<term>Neural network</term>
<term>Plasticity</term>
<term>Tactile sensitivity</term>
<term>Timed system</term>
<term>Transformation of coordinates</term>
<term>Virtual image</term>
</keywords><keywords scheme="Pascal" xml:lang="fr"><term>Image virtuelle</term>
<term>Intelligence artificielle</term>
<term>Système intelligent</term>
<term>Système temporisé</term>
<term>Inélasticité</term>
<term>Plasticité</term>
<term>Sensibilité tactile</term>
<term>Système biologique</term>
<term>Méthode adaptative</term>
<term>Changement coordonnée</term>
<term>Réseau neuronal</term>
<term>.</term>
</keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Intelligence artificielle</term>
</keywords><front><div type="abstract" xml:lang="en">A spiking neural network (SNN) model trained with spiking-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 in order to create a virtual image map of a haptic input. The position of the haptic input is used to train the SNN using STDP 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. This principle can be applied to complex co-ordinate transformations in artificial intelligent systems to process biological stimuli.</div>
</front><inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>0302-9743</s0>
</fA01><fA05><s2>3610</s2>
</fA05><fA08 i1="01" i2="1" l="ENG"><s1>Adaptive Co-ordinate transformation based on a spike timing-dependent plasticity learning paradigm</s1>
</fA08><fA09 i1="01" i2="1" l="ENG"><s1>Advances in natural computation : Changsha, 27-29 August 2005</s1>
</fA09><fA11 i1="01" i2="1"><s1>QINGXIANG WU</s1>
</fA11><fA11 i1="02" i2="1"><s1>MCGINNITY (T. M.)</s1>
</fA11><fA11 i1="03" i2="1"><s1>MAGUIRE (L. P.)</s1>
</fA11><fA11 i1="04" i2="1"><s1>BELATRECHE (A.)</s1>
</fA11><fA11 i1="05" i2="1"><s1>GLACKIN (B.)</s1>
</fA11><fA12 i1="01" i2="1"><s1>WANG LIPO</s1>
<s9>ed.</s9>
</fA12><fA12 i1="02" i2="1"><s1>CHEN KE</s1>
<s9>ed.</s9>
</fA12><fA12 i1="03" i2="1"><s1>ONG YEW SOON</s1>
<s9>ed.</s9>
</fA12><fA14 i1="01"><s1>School of Computing and Intelligent Systems, University of Ulster, Magee Campus Derry</s1>
<s2>BT48 7JL, N.Ireland</s2>
<s3>GBR</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>4 aut.</sZ>
<sZ>5 aut.</sZ>
</fA14><fA20><s2>Part 1, 420-428</s2>
</fA20><fA21><s1>2005</s1>
</fA21><fA23 i1="01"><s0>ENG</s0>
</fA23><fA26 i1="01"><s0>3-540-28323-4</s0>
</fA26><fA43 i1="01"><s1>INIST</s1>
<s2>16343</s2>
<s5>354000124507310540</s5>
</fA43><fA44><s0>0000</s0>
<s1>© 2005 INIST-CNRS. All rights reserved.</s1>
</fA44><fA45><s0>22 ref.</s0>
</fA45><fA47 i1="01" i2="1"><s0>05-0422213</s0>
</fA47><fA60><s1>P</s1>
<s2>C</s2>
</fA60><fA64 i1="01" i2="1"><s0>Lecture notes in computer science</s0>
</fA64><fA66 i1="01"><s0>DEU</s0>
</fA66><fC01 i1="01" l="ENG"><s0>A spiking neural network (SNN) model trained with spiking-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 in order to create a virtual image map of a haptic input. The position of the haptic input is used to train the SNN using STDP 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. This principle can be applied to complex co-ordinate transformations in artificial intelligent systems to process biological stimuli.</s0>
</fC01><fC02 i1="01" i2="X"><s0>001D02C</s0>
</fC02><fC03 i1="01" i2="X" l="FRE"><s0>Image virtuelle</s0>
<s5>06</s5>
</fC03><fC03 i1="01" i2="X" l="ENG"><s0>Virtual image</s0>
<s5>06</s5>
</fC03><fC03 i1="01" i2="X" l="SPA"><s0>Imagen virtual</s0>
<s5>06</s5>
</fC03><fC03 i1="02" i2="X" l="FRE"><s0>Intelligence artificielle</s0>
<s5>07</s5>
</fC03><fC03 i1="02" i2="X" l="ENG"><s0>Artificial intelligence</s0>
<s5>07</s5>
</fC03><fC03 i1="02" i2="X" l="SPA"><s0>Inteligencia artificial</s0>
<s5>07</s5>
</fC03><fC03 i1="03" i2="X" l="FRE"><s0>Système intelligent</s0>
<s5>08</s5>
</fC03><fC03 i1="03" i2="X" l="ENG"><s0>Intelligent system</s0>
<s5>08</s5>
</fC03><fC03 i1="03" i2="X" l="SPA"><s0>Sistema inteligente</s0>
<s5>08</s5>
</fC03><fC03 i1="04" i2="X" l="FRE"><s0>Système temporisé</s0>
<s5>18</s5>
</fC03><fC03 i1="04" i2="X" l="ENG"><s0>Timed system</s0>
<s5>18</s5>
</fC03><fC03 i1="04" i2="X" l="SPA"><s0>Sistema temporizado</s0>
<s5>18</s5>
</fC03><fC03 i1="05" i2="X" l="FRE"><s0>Inélasticité</s0>
<s5>19</s5>
</fC03><fC03 i1="05" i2="X" l="ENG"><s0>Inelasticity</s0>
<s5>19</s5>
</fC03><fC03 i1="05" i2="X" l="SPA"><s0>Inelasticidad</s0>
<s5>19</s5>
</fC03><fC03 i1="06" i2="X" l="FRE"><s0>Plasticité</s0>
<s5>20</s5>
</fC03><fC03 i1="06" i2="X" l="ENG"><s0>Plasticity</s0>
<s5>20</s5>
</fC03><fC03 i1="06" i2="X" l="SPA"><s0>Plasticidad</s0>
<s5>20</s5>
</fC03><fC03 i1="07" i2="X" l="FRE"><s0>Sensibilité tactile</s0>
<s5>21</s5>
</fC03><fC03 i1="07" i2="X" l="ENG"><s0>Tactile sensitivity</s0>
<s5>21</s5>
</fC03><fC03 i1="07" i2="X" l="SPA"><s0>Sensibilidad tactil</s0>
<s5>21</s5>
</fC03><fC03 i1="08" i2="X" l="FRE"><s0>Système biologique</s0>
<s5>22</s5>
</fC03><fC03 i1="08" i2="X" l="ENG"><s0>Biological system</s0>
<s5>22</s5>
</fC03><fC03 i1="08" i2="X" l="SPA"><s0>Sistema biológico</s0>
<s5>22</s5>
</fC03><fC03 i1="09" i2="X" l="FRE"><s0>Méthode adaptative</s0>
<s5>23</s5>
</fC03><fC03 i1="09" i2="X" l="ENG"><s0>Adaptive method</s0>
<s5>23</s5>
</fC03><fC03 i1="09" i2="X" l="SPA"><s0>Método adaptativo</s0>
<s5>23</s5>
</fC03><fC03 i1="10" i2="X" l="FRE"><s0>Changement coordonnée</s0>
<s5>24</s5>
</fC03><fC03 i1="10" i2="X" l="ENG"><s0>Transformation of coordinates</s0>
<s5>24</s5>
</fC03><fC03 i1="10" i2="X" l="SPA"><s0>Cambio coordenadas</s0>
<s5>24</s5>
</fC03><fC03 i1="11" i2="X" l="FRE"><s0>Réseau neuronal</s0>
<s5>25</s5>
</fC03><fC03 i1="11" i2="X" l="ENG"><s0>Neural network</s0>
<s5>25</s5>
</fC03><fC03 i1="11" i2="X" l="SPA"><s0>Red neuronal</s0>
<s5>25</s5>
</fC03><fC03 i1="12" i2="X" l="FRE"><s0>.</s0>
<s4>INC</s4>
<s5>82</s5>
</fC03><fN21><s1>290</s1>
</fN21><fN44 i1="01"><s1>OTO</s1>
</fN44><fN82><s1>OTO</s1>
</fN82><pR><fA30 i1="01" i2="1" l="ENG"><s1>ICNC : international conference on advances in natural computation</s1>
<s2>1</s2>
<s3>Changsha CHN</s3>
<s4>2005-08-27</s4>
</fA30>
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