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dc.contributor.authorMoujahid, Abdelmalik ORCID
dc.contributor.authorD Anjou, A.
dc.contributor.authorTorrealdea Folgado, Francisco Javier
dc.contributor.authorTorrealdea, Francisco
dc.date.accessioned2014-03-17T10:53:34Z
dc.date.available2014-03-17T10:53:34Z
dc.date.issued2011-03-21
dc.identifier.citationA. Moujahid, A. d’Anjou, F. Torrealdea, F.J. Torrealdea. “Energy and information in Hodgking-Huxley neurons”. Physical Review E 83, 031912 (2011)es
dc.identifier.issn1550-2376
dc.identifier.urihttp://hdl.handle.net/10810/11724
dc.description.abstract[EN]The generation of spikes by neurons is energetically a costly process and the evaluation of the metabolic energy required to maintain the signaling activity of neurons a challenge of practical interest. Neuron models are frequently used to represent the dynamics of real neurons but hardly ever to evaluate the electrochemical energy required to maintain that dynamics. This paper discusses the interpretation of a Hodgkin-Huxley circuit as an energy model for real biological neurons and uses it to evaluate the consumption of metabolic energy in the transmission of information between neurons coupled by electrical synapses, i.e., gap junctions. We show that for a single postsynaptic neuron maximum energy efficiency, measured in bits of mutual information per molecule of adenosine triphosphate (ATP) consumed, requires maximum energy consumption. For groups of parallel postsynaptic neurons we determine values of the synaptic conductance at which the energy efficiency of the transmission presents clear maxima at relatively very low values of metabolic energy consumption. Contrary to what could be expected, the best performance occurs at a low energy cost.es
dc.language.isoenges
dc.publisherAmerican Physical Societyes
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.subjectneuron metabolic energyes
dc.subjectaction potentiales
dc.subjectinformation energyes
dc.titleEnergy and information in Hodgkin-Huxley neuronses
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder©2011 American Physical Societyes
dc.relation.publisherversionhttp://journals.aps.org/pre/es
dc.identifier.doi10.1103/PhysRevE.83.031912
dc.departamentoesCiencia de la computación e inteligencia artificiales_ES
dc.departamentoeuKonputazio zientziak eta adimen artifizialaes_ES


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