InforLorV4, PascalFrancis, Corpus, bibRecord, 000154

On the Performance of Voltage Stepping for the Simulation of Adaptive, Nonlinear Integrate-and-Fire Neuronal Networks

Identifieur interne : 000154 ( PascalFrancis/Corpus ); précédent : 000153; suivant : 000155

On the Performance of Voltage Stepping for the Simulation of Adaptive, Nonlinear Integrate-and-Fire Neuronal Networks

Auteurs : MOHAMED GHAITH KAABI ; Arnaud Tonnelier ; Dominique Martinez

Source :

Neural computation [ 0899-7667 ] ; 2011.

RBID : Pascal:11-0202710

Descripteurs français

Pascal (Inist)
- Réseau neuronal, Calcul neuronal, Neurone impulsionnel, Adaptation, Synapse, Système événement discret, Spécification, Méthode Runge Kutta, Connectivité graphe, Potentiel action, Timing, Neurone integrate-and-fire.

English descriptors

KwdEn :
- Adaptation, Discrete event system, Graph connectivity, Integrate-and-fire neuron, Neural computation, Neural network, Runge Kutta method, Specification, Spike, Spiking neuron, Synapse, Timing.

Abstract

In traditional event-driven strategies, spike timings are analytically given or calculated with arbitrary precision (up to machine precision). Exact computation is possible only for simplified neuron models, mainly the leaky integrate-and-fire model. In a recent paper, Zheng, Tonnelier, and Martinez (2009) introduced an approximate event-driven strategy, named voltage stepping, that allows the generic simulation of nonlinear spiking neurons. Promising results were achieved in the simulation of single quadratic integrate-and-fire neurons. Here, we assess the performance of voltage stepping in network simulations by considering more complex neurons (quadratic integrate-and-fire neurons with adaptation) coupled with multiple synapses. To handle the discrete nature of synaptic interactions, we recast voltage stepping in a general framework, the discrete event system specification. The efficiency of the method is assessed through simulations and comparisons with a modified time-stepping scheme of the Runge-Kutta type. We demonstrated numerically that the original order of voltage stepping is preserved when simulating connected spiking neurons, independent of the network activity and connectivity.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

A01	`01`	`1`		`@0 0899-7667`
A03		`1`		`@0 Neural comput.`
A05				`@2 23`
A06				`@2 5`
A08	`01`	`1`	`ENG`	`@1 On the Performance of Voltage Stepping for the Simulation of Adaptive, Nonlinear Integrate-and-Fire Neuronal Networks`
A11	`01`	`1`		`@1 MOHAMED GHAITH KAABI`
A11	`02`	`1`		`@1 TONNELIER (Arnaud)`
A11	`03`	`1`		`@1 MARTINEZ (Dominique)`
A14	`01`			`@1 Unité Mixte de Recherche 7503, LORIA, CNRS @2 54506 Vandoeuvre-lès-Nancy @3 FRA @Z 1 aut.`
A14	`02`			`@1 INRIA @2 38334 Saint Ismier @3 FRA @Z 2 aut.`
A14	`03`			`@1 Unité Mixte de Recherche 7503, LORIA, CNRS, 54506 Unndoeamre-lès-Narvcy, France, and Unité Mixte de Recherche 1272, Physiologie de l'Insecte Signalisation et Communication, Institut National de la Recherche Agronomique @2 78026 Versailles @3 FRA @Z 3 aut.`
A20				`@1 1187-1204`
A21				`@1 2011`
A23	`01`			`@0 ENG`
A43	`01`			`@1 INIST @2 22595 @5 354000192931180030`
A44				`@0 0000 @1 © 2011 INIST-CNRS. All rights reserved.`
A45				`@0 1 p.1/4`
A47	`01`	`1`		`@0 11-0202710`
A60				`@1 P`
A61				`@0 A`
A64	`01`	`1`		`@0 Neural computation`
A66	`01`			`@0 USA`
C01	`01`		`ENG`	@0 In traditional event-driven strategies, spike timings are analytically given or calculated with arbitrary precision (up to machine precision). Exact computation is possible only for simplified neuron models, mainly the leaky integrate-and-fire model. In a recent paper, Zheng, Tonnelier, and Martinez (2009) introduced an approximate event-driven strategy, named voltage stepping, that allows the generic simulation of nonlinear spiking neurons. Promising results were achieved in the simulation of single quadratic integrate-and-fire neurons. Here, we assess the performance of voltage stepping in network simulations by considering more complex neurons (quadratic integrate-and-fire neurons with adaptation) coupled with multiple synapses. To handle the discrete nature of synaptic interactions, we recast voltage stepping in a general framework, the discrete event system specification. The efficiency of the method is assessed through simulations and comparisons with a modified time-stepping scheme of the Runge-Kutta type. We demonstrated numerically that the original order of voltage stepping is preserved when simulating connected spiking neurons, independent of the network activity and connectivity.
C02	`01`	`X`		`@0 001D02C07`
C02	`02`	`X`		`@0 001D02C02`
C02	`03`	`X`		`@0 002A01B`
C03	`01`	`X`	`FRE`	`@0 Réseau neuronal @5 01`
C03	`01`	`X`	`ENG`	`@0 Neural network @5 01`
C03	`01`	`X`	`SPA`	`@0 Red neuronal @5 01`
C03	`02`	`X`	`FRE`	`@0 Calcul neuronal @5 02`
C03	`02`	`X`	`ENG`	`@0 Neural computation @5 02`
C03	`02`	`X`	`SPA`	`@0 computación neuronal @5 02`
C03	`03`	`X`	`FRE`	`@0 Neurone impulsionnel @5 17`
C03	`03`	`X`	`ENG`	`@0 Spiking neuron @5 17`
C03	`03`	`X`	`SPA`	`@0 Neurona pulsante @5 17`
C03	`04`	`X`	`FRE`	`@0 Adaptation @5 18`
C03	`04`	`X`	`ENG`	`@0 Adaptation @5 18`
C03	`04`	`X`	`SPA`	`@0 Adaptación @5 18`
C03	`05`	`X`	`FRE`	`@0 Synapse @5 19`
C03	`05`	`X`	`ENG`	`@0 Synapse @5 19`
C03	`05`	`X`	`SPA`	`@0 Sinapsis @5 19`
C03	`06`	`X`	`FRE`	`@0 Système événement discret @5 20`
C03	`06`	`X`	`ENG`	`@0 Discrete event system @5 20`
C03	`06`	`X`	`SPA`	`@0 Sistema acontecimiento discreto @5 20`
C03	`07`	`X`	`FRE`	`@0 Spécification @5 21`
C03	`07`	`X`	`ENG`	`@0 Specification @5 21`
C03	`07`	`X`	`SPA`	`@0 Especificación @5 21`
C03	`08`	`X`	`FRE`	`@0 Méthode Runge Kutta @5 22`
C03	`08`	`X`	`ENG`	`@0 Runge Kutta method @5 22`
C03	`08`	`X`	`SPA`	`@0 Método Runge Kutta @5 22`
C03	`09`	`X`	`FRE`	`@0 Connectivité graphe @5 23`
C03	`09`	`X`	`ENG`	`@0 Graph connectivity @5 23`
C03	`09`	`X`	`SPA`	`@0 Conectividad grafo @5 23`
C03	`10`	`X`	`FRE`	`@0 Potentiel action @4 CD @5 96`
C03	`10`	`X`	`ENG`	`@0 Spike @4 CD @5 96`
C03	`11`	`X`	`FRE`	`@0 Timing @4 CD @5 97`
C03	`11`	`X`	`ENG`	`@0 Timing @4 CD @5 97`
C03	`12`	`X`	`FRE`	`@0 Neurone integrate-and-fire @4 CD @5 98`
C03	`12`	`X`	`ENG`	`@0 Integrate-and-fire neuron @4 CD @5 98`
N21				`@1 136`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

Format Inist (serveur)

NO :	PASCAL 11-0202710 INIST
ET :	On the Performance of Voltage Stepping for the Simulation of Adaptive, Nonlinear Integrate-and-Fire Neuronal Networks
AU :	MOHAMED GHAITH KAABI; TONNELIER (Arnaud); MARTINEZ (Dominique)
AF :	Unité Mixte de Recherche 7503, LORIA, CNRS/54506 Vandoeuvre-lès-Nancy/France (1 aut.); INRIA/38334 Saint Ismier/France (2 aut.); Unité Mixte de Recherche 7503, LORIA, CNRS, 54506 Unndoeamre-lès-Narvcy, France, and Unité Mixte de Recherche 1272, Physiologie de l'Insecte Signalisation et Communication, Institut National de la Recherche Agronomique/78026 Versailles/France (3 aut.)
DT :	Publication en série; Niveau analytique
SO :	Neural computation; ISSN 0899-7667; Etats-Unis; Da. 2011; Vol. 23; No. 5; Pp. 1187-1204; Bibl. 1 p.1/4
LA :	Anglais
EA :	In traditional event-driven strategies, spike timings are analytically given or calculated with arbitrary precision (up to machine precision). Exact computation is possible only for simplified neuron models, mainly the leaky integrate-and-fire model. In a recent paper, Zheng, Tonnelier, and Martinez (2009) introduced an approximate event-driven strategy, named voltage stepping, that allows the generic simulation of nonlinear spiking neurons. Promising results were achieved in the simulation of single quadratic integrate-and-fire neurons. Here, we assess the performance of voltage stepping in network simulations by considering more complex neurons (quadratic integrate-and-fire neurons with adaptation) coupled with multiple synapses. To handle the discrete nature of synaptic interactions, we recast voltage stepping in a general framework, the discrete event system specification. The efficiency of the method is assessed through simulations and comparisons with a modified time-stepping scheme of the Runge-Kutta type. We demonstrated numerically that the original order of voltage stepping is preserved when simulating connected spiking neurons, independent of the network activity and connectivity.
CC :	001D02C07; 001D02C02; 002A01B
FD :	Réseau neuronal; Calcul neuronal; Neurone impulsionnel; Adaptation; Synapse; Système événement discret; Spécification; Méthode Runge Kutta; Connectivité graphe; Potentiel action; Timing; Neurone integrate-and-fire
ED :	Neural network; Neural computation; Spiking neuron; Adaptation; Synapse; Discrete event system; Specification; Runge Kutta method; Graph connectivity; Spike; Timing; Integrate-and-fire neuron
SD :	Red neuronal; computación neuronal; Neurona pulsante; Adaptación; Sinapsis; Sistema acontecimiento discreto; Especificación; Método Runge Kutta; Conectividad grafo
LO :	INIST-22595.354000192931180030
ID :	11-0202710

Links to Exploration step

Pascal:11-0202710

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">On the Performance of Voltage Stepping for the Simulation of Adaptive, Nonlinear Integrate-and-Fire Neuronal Networks</title>
<author><name sortKey="Mohamed Ghaith Kaabi" sort="Mohamed Ghaith Kaabi" uniqKey="Mohamed Ghaith Kaabi" last="Mohamed Ghaith Kaabi">MOHAMED GHAITH KAABI</name>
<affiliation><inist:fA14 i1="01"><s1>Unité Mixte de Recherche 7503, LORIA, CNRS</s1>
<s2>54506 Vandoeuvre-lès-Nancy</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
<author><name sortKey="Tonnelier, Arnaud" sort="Tonnelier, Arnaud" uniqKey="Tonnelier A" first="Arnaud" last="Tonnelier">Arnaud Tonnelier</name>
<affiliation><inist:fA14 i1="02"><s1>INRIA</s1>
<s2>38334 Saint Ismier</s2>
<s3>FRA</s3>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
<author><name sortKey="Martinez, Dominique" sort="Martinez, Dominique" uniqKey="Martinez D" first="Dominique" last="Martinez">Dominique Martinez</name>
<affiliation><inist:fA14 i1="03"><s1>Unité Mixte de Recherche 7503, LORIA, CNRS, 54506 Unndoeamre-lès-Narvcy, France, and Unité Mixte de Recherche 1272, Physiologie de l'Insecte Signalisation et Communication, Institut National de la Recherche Agronomique</s1>
<s2>78026 Versailles</s2>
<s3>FRA</s3>
<sZ>3 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">INIST</idno>
<idno type="inist">11-0202710</idno>
<date when="2011">2011</date>
<idno type="stanalyst">PASCAL 11-0202710 INIST</idno>
<idno type="RBID">Pascal:11-0202710</idno>
<idno type="wicri:Area/PascalFrancis/Corpus">000154</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">On the Performance of Voltage Stepping for the Simulation of Adaptive, Nonlinear Integrate-and-Fire Neuronal Networks</title>
<author><name sortKey="Mohamed Ghaith Kaabi" sort="Mohamed Ghaith Kaabi" uniqKey="Mohamed Ghaith Kaabi" last="Mohamed Ghaith Kaabi">MOHAMED GHAITH KAABI</name>
<affiliation><inist:fA14 i1="01"><s1>Unité Mixte de Recherche 7503, LORIA, CNRS</s1>
<s2>54506 Vandoeuvre-lès-Nancy</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
<author><name sortKey="Tonnelier, Arnaud" sort="Tonnelier, Arnaud" uniqKey="Tonnelier A" first="Arnaud" last="Tonnelier">Arnaud Tonnelier</name>
<affiliation><inist:fA14 i1="02"><s1>INRIA</s1>
<s2>38334 Saint Ismier</s2>
<s3>FRA</s3>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
<author><name sortKey="Martinez, Dominique" sort="Martinez, Dominique" uniqKey="Martinez D" first="Dominique" last="Martinez">Dominique Martinez</name>
<affiliation><inist:fA14 i1="03"><s1>Unité Mixte de Recherche 7503, LORIA, CNRS, 54506 Unndoeamre-lès-Narvcy, France, and Unité Mixte de Recherche 1272, Physiologie de l'Insecte Signalisation et Communication, Institut National de la Recherche Agronomique</s1>
<s2>78026 Versailles</s2>
<s3>FRA</s3>
<sZ>3 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
</analytic>
<series><title level="j" type="main">Neural computation</title>
<title level="j" type="abbreviated">Neural comput.</title>
<idno type="ISSN">0899-7667</idno>
<imprint><date when="2011">2011</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt><title level="j" type="main">Neural computation</title>
<title level="j" type="abbreviated">Neural comput.</title>
<idno type="ISSN">0899-7667</idno>
</seriesStmt>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adaptation</term>
<term>Discrete event system</term>
<term>Graph connectivity</term>
<term>Integrate-and-fire neuron</term>
<term>Neural computation</term>
<term>Neural network</term>
<term>Runge Kutta method</term>
<term>Specification</term>
<term>Spike</term>
<term>Spiking neuron</term>
<term>Synapse</term>
<term>Timing</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr"><term>Réseau neuronal</term>
<term>Calcul neuronal</term>
<term>Neurone impulsionnel</term>
<term>Adaptation</term>
<term>Synapse</term>
<term>Système événement discret</term>
<term>Spécification</term>
<term>Méthode Runge Kutta</term>
<term>Connectivité graphe</term>
<term>Potentiel action</term>
<term>Timing</term>
<term>Neurone integrate-and-fire</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">In traditional event-driven strategies, spike timings are analytically given or calculated with arbitrary precision (up to machine precision). Exact computation is possible only for simplified neuron models, mainly the leaky integrate-and-fire model. In a recent paper, Zheng, Tonnelier, and Martinez (2009) introduced an approximate event-driven strategy, named voltage stepping, that allows the generic simulation of nonlinear spiking neurons. Promising results were achieved in the simulation of single quadratic integrate-and-fire neurons. Here, we assess the performance of voltage stepping in network simulations by considering more complex neurons (quadratic integrate-and-fire neurons with adaptation) coupled with multiple synapses. To handle the discrete nature of synaptic interactions, we recast voltage stepping in a general framework, the discrete event system specification. The efficiency of the method is assessed through simulations and comparisons with a modified time-stepping scheme of the Runge-Kutta type. We demonstrated numerically that the original order of voltage stepping is preserved when simulating connected spiking neurons, independent of the network activity and connectivity.</div>
</front>
</TEI>
<inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>0899-7667</s0>
</fA01>
<fA03 i2="1"><s0>Neural comput.</s0>
</fA03>
<fA05><s2>23</s2>
</fA05>
<fA06><s2>5</s2>
</fA06>
<fA08 i1="01" i2="1" l="ENG"><s1>On the Performance of Voltage Stepping for the Simulation of Adaptive, Nonlinear Integrate-and-Fire Neuronal Networks</s1>
</fA08>
<fA11 i1="01" i2="1"><s1>MOHAMED GHAITH KAABI</s1>
</fA11>
<fA11 i1="02" i2="1"><s1>TONNELIER (Arnaud)</s1>
</fA11>
<fA11 i1="03" i2="1"><s1>MARTINEZ (Dominique)</s1>
</fA11>
<fA14 i1="01"><s1>Unité Mixte de Recherche 7503, LORIA, CNRS</s1>
<s2>54506 Vandoeuvre-lès-Nancy</s2>
<s3>FRA</s3>
<sZ>1 aut.</sZ>
</fA14>
<fA14 i1="02"><s1>INRIA</s1>
<s2>38334 Saint Ismier</s2>
<s3>FRA</s3>
<sZ>2 aut.</sZ>
</fA14>
<fA14 i1="03"><s1>Unité Mixte de Recherche 7503, LORIA, CNRS, 54506 Unndoeamre-lès-Narvcy, France, and Unité Mixte de Recherche 1272, Physiologie de l'Insecte Signalisation et Communication, Institut National de la Recherche Agronomique</s1>
<s2>78026 Versailles</s2>
<s3>FRA</s3>
<sZ>3 aut.</sZ>
</fA14>
<fA20><s1>1187-1204</s1>
</fA20>
<fA21><s1>2011</s1>
</fA21>
<fA23 i1="01"><s0>ENG</s0>
</fA23>
<fA43 i1="01"><s1>INIST</s1>
<s2>22595</s2>
<s5>354000192931180030</s5>
</fA43>
<fA44><s0>0000</s0>
<s1>© 2011 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45><s0>1 p.1/4</s0>
</fA45>
<fA47 i1="01" i2="1"><s0>11-0202710</s0>
</fA47>
<fA60><s1>P</s1>
</fA60>
<fA61><s0>A</s0>
</fA61>
<fA64 i1="01" i2="1"><s0>Neural computation</s0>
</fA64>
<fA66 i1="01"><s0>USA</s0>
</fA66>
<fC01 i1="01" l="ENG"><s0>In traditional event-driven strategies, spike timings are analytically given or calculated with arbitrary precision (up to machine precision). Exact computation is possible only for simplified neuron models, mainly the leaky integrate-and-fire model. In a recent paper, Zheng, Tonnelier, and Martinez (2009) introduced an approximate event-driven strategy, named voltage stepping, that allows the generic simulation of nonlinear spiking neurons. Promising results were achieved in the simulation of single quadratic integrate-and-fire neurons. Here, we assess the performance of voltage stepping in network simulations by considering more complex neurons (quadratic integrate-and-fire neurons with adaptation) coupled with multiple synapses. To handle the discrete nature of synaptic interactions, we recast voltage stepping in a general framework, the discrete event system specification. The efficiency of the method is assessed through simulations and comparisons with a modified time-stepping scheme of the Runge-Kutta type. We demonstrated numerically that the original order of voltage stepping is preserved when simulating connected spiking neurons, independent of the network activity and connectivity.</s0>
</fC01>
<fC02 i1="01" i2="X"><s0>001D02C07</s0>
</fC02>
<fC02 i1="02" i2="X"><s0>001D02C02</s0>
</fC02>
<fC02 i1="03" i2="X"><s0>002A01B</s0>
</fC02>
<fC03 i1="01" i2="X" l="FRE"><s0>Réseau neuronal</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="ENG"><s0>Neural network</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="SPA"><s0>Red neuronal</s0>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="X" l="FRE"><s0>Calcul neuronal</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG"><s0>Neural computation</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA"><s0>computación neuronal</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE"><s0>Neurone impulsionnel</s0>
<s5>17</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG"><s0>Spiking neuron</s0>
<s5>17</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA"><s0>Neurona pulsante</s0>
<s5>17</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Adaptation</s0>
<s5>18</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Adaptation</s0>
<s5>18</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Adaptación</s0>
<s5>18</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Synapse</s0>
<s5>19</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Synapse</s0>
<s5>19</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Sinapsis</s0>
<s5>19</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Système événement discret</s0>
<s5>20</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Discrete event system</s0>
<s5>20</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Sistema acontecimiento discreto</s0>
<s5>20</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Spécification</s0>
<s5>21</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Specification</s0>
<s5>21</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Especificación</s0>
<s5>21</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Méthode Runge Kutta</s0>
<s5>22</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Runge Kutta method</s0>
<s5>22</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Método Runge Kutta</s0>
<s5>22</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Connectivité graphe</s0>
<s5>23</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Graph connectivity</s0>
<s5>23</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Conectividad grafo</s0>
<s5>23</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Potentiel action</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Spike</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Timing</s0>
<s4>CD</s4>
<s5>97</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Timing</s0>
<s4>CD</s4>
<s5>97</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Neurone integrate-and-fire</s0>
<s4>CD</s4>
<s5>98</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Integrate-and-fire neuron</s0>
<s4>CD</s4>
<s5>98</s5>
</fC03>
<fN21><s1>136</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
<server><NO>PASCAL 11-0202710 INIST</NO>
<ET>On the Performance of Voltage Stepping for the Simulation of Adaptive, Nonlinear Integrate-and-Fire Neuronal Networks</ET>
<AU>MOHAMED GHAITH KAABI; TONNELIER (Arnaud); MARTINEZ (Dominique)</AU>
<AF>Unité Mixte de Recherche 7503, LORIA, CNRS/54506 Vandoeuvre-lès-Nancy/France (1 aut.); INRIA/38334 Saint Ismier/France (2 aut.); Unité Mixte de Recherche 7503, LORIA, CNRS, 54506 Unndoeamre-lès-Narvcy, France, and Unité Mixte de Recherche 1272, Physiologie de l'Insecte Signalisation et Communication, Institut National de la Recherche Agronomique/78026 Versailles/France (3 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Neural computation; ISSN 0899-7667; Etats-Unis; Da. 2011; Vol. 23; No. 5; Pp. 1187-1204; Bibl. 1 p.1/4</SO>
<LA>Anglais</LA>
<EA>In traditional event-driven strategies, spike timings are analytically given or calculated with arbitrary precision (up to machine precision). Exact computation is possible only for simplified neuron models, mainly the leaky integrate-and-fire model. In a recent paper, Zheng, Tonnelier, and Martinez (2009) introduced an approximate event-driven strategy, named voltage stepping, that allows the generic simulation of nonlinear spiking neurons. Promising results were achieved in the simulation of single quadratic integrate-and-fire neurons. Here, we assess the performance of voltage stepping in network simulations by considering more complex neurons (quadratic integrate-and-fire neurons with adaptation) coupled with multiple synapses. To handle the discrete nature of synaptic interactions, we recast voltage stepping in a general framework, the discrete event system specification. The efficiency of the method is assessed through simulations and comparisons with a modified time-stepping scheme of the Runge-Kutta type. We demonstrated numerically that the original order of voltage stepping is preserved when simulating connected spiking neurons, independent of the network activity and connectivity.</EA>
<CC>001D02C07; 001D02C02; 002A01B</CC>
<FD>Réseau neuronal; Calcul neuronal; Neurone impulsionnel; Adaptation; Synapse; Système événement discret; Spécification; Méthode Runge Kutta; Connectivité graphe; Potentiel action; Timing; Neurone integrate-and-fire</FD>
<ED>Neural network; Neural computation; Spiking neuron; Adaptation; Synapse; Discrete event system; Specification; Runge Kutta method; Graph connectivity; Spike; Timing; Integrate-and-fire neuron</ED>
<SD>Red neuronal; computación neuronal; Neurona pulsante; Adaptación; Sinapsis; Sistema acontecimiento discreto; Especificación; Método Runge Kutta; Conectividad grafo</SD>
<LO>INIST-22595.354000192931180030</LO>
<ID>11-0202710</ID>
</server>
</inist>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/PascalFrancis/Corpus

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000154 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/PascalFrancis/Corpus/biblio.hfd -nk 000154 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Wicri/Lorraine
   |area=    InforLorV4
   |flux=    PascalFrancis
   |étape=   Corpus
   |type=    RBID
   |clé=     Pascal:11-0202710
   |texte=   On the Performance of Voltage Stepping for the Simulation of Adaptive, Nonlinear Integrate-and-Fire Neuronal Networks
}}

This area was generated with Dilib version V0.6.33.
Data generation: Mon Jun 10 21:56:28 2019. Site generation: Fri Feb 25 15:29:27 2022

	Serveur d'exploration sur la recherche en informatique en Lorraine
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.

Serveur d'exploration sur la recherche en informatique en Lorraine

On the Performance of Voltage Stepping for the Simulation of Adaptive, Nonlinear Integrate-and-Fire Neuronal Networks

On the Performance of Voltage Stepping for the Simulation of Adaptive, Nonlinear Integrate-and-Fire Neuronal Networks

Source :

Descripteurs français

English descriptors

Abstract

Notice en format standard (ISO 2709)

Format Inist (serveur)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri