Overview of facts and issues about neural coding by spikes
Identifieur interne : 002E79 ( Main/Exploration ); précédent : 002E78; suivant : 002E80Overview of facts and issues about neural coding by spikes
Auteurs : Bruno Cessac [France] ; Hélène Paugam-Moisy [France] ; Thierry Viéville [France]Source :
- Journal of Physiology - Paris [ 0928-4257 ] ; 2010.
English descriptors
Abstract
In the present overview, our wish is to demystify some aspects of coding with spike-timing, through a simple review of well-understood technical facts regarding spike coding. Our goal is a better understanding of the extent to which computing and modeling with spiking neuron networks might be biologically plausible and computationally efficient. We intentionally restrict ourselves to a deterministic implementation of spiking neuron networks and we consider that the dynamics of a network is defined by a non-stochastic mapping. By staying in this rather simple framework, we are able to propose results, formula and concrete numerical values, on several topics: (i) general time constraints, (ii) links between continuous signals and spike trains, (iii) spiking neuron networks parameter adjustment. Beside an argued review of several facts and issues about neural coding by spikes, we propose new results, such as a numerical evaluation of the most critical temporal variables that schedule the progress of realistic spike trains. When implementing spiking neuron networks, for biological simulation or computational purpose, it is important to take into account the indisputable facts here unfolded. This precaution could prevent one from implementing mechanisms that would be meaningless relative to obvious time constraints, or from artificially introducing spikes when continuous calculations would be sufficient and more simple. It is also pointed out that implementing a large-scale spiking neuron network is finally a simple task.
Url:
DOI: 10.1016/j.jphysparis.2009.11.002
Affiliations:
- France
- Grand Est, Lorraine (région), Provence-Alpes-Côte d'Azur
- Nancy, Nice
- Institut national polytechnique de Lorraine, Université Nancy 2, Université Nice Sophia Antipolis, Université de Lorraine
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Le document en format XML
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<idno type="DOI">10.1016/j.jphysparis.2009.11.002</idno>
<series><title level="j">Journal of Physiology - Paris</title>
<idno type="ISSN">0928-4257</idno>
<imprint><date type="datePub">2010</date>
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<profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Neural code</term>
<term>Spike train metrics</term>
<term>Spiking neuron networks</term>
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<front><div type="abstract" xml:lang="en">In the present overview, our wish is to demystify some aspects of coding with spike-timing, through a simple review of well-understood technical facts regarding spike coding. Our goal is a better understanding of the extent to which computing and modeling with spiking neuron networks might be biologically plausible and computationally efficient. We intentionally restrict ourselves to a deterministic implementation of spiking neuron networks and we consider that the dynamics of a network is defined by a non-stochastic mapping. By staying in this rather simple framework, we are able to propose results, formula and concrete numerical values, on several topics: (i) general time constraints, (ii) links between continuous signals and spike trains, (iii) spiking neuron networks parameter adjustment. Beside an argued review of several facts and issues about neural coding by spikes, we propose new results, such as a numerical evaluation of the most critical temporal variables that schedule the progress of realistic spike trains. When implementing spiking neuron networks, for biological simulation or computational purpose, it is important to take into account the indisputable facts here unfolded. This precaution could prevent one from implementing mechanisms that would be meaningless relative to obvious time constraints, or from artificially introducing spikes when continuous calculations would be sufficient and more simple. It is also pointed out that implementing a large-scale spiking neuron network is finally a simple task.</div>
</front>
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<tree><country name="France"><region name="Provence-Alpes-Côte d'Azur"><name sortKey="Cessac, Bruno" sort="Cessac, Bruno" uniqKey="Cessac B" first="Bruno" last="Cessac">Bruno Cessac</name>
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