Impact of frequency on the energetic efficiency of action potentials

Anand Singh*, Pierre J. Magistretti, Bruno Weber, Renaud Jolivet

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Sodium entry during the decaying phase of the action potential determines the metabolic efficiency of a neuron's spiking mechanism. Recent studies have reported that mammalian action potentials are close to metabolic optimality but that fast-spiking inhibitory neurons are less efficient than their pyramidal counterparts. It is postulated that this represents nature's tradeoff between metabolic efficiency and the ability to discharge at high rates. Using eight different published Hodgkin-Huxley models of mammalian neurons to cover a wide range of action potential metabolic efficiencies, we show that the cost of operating a neuron is heavily dependent on its output frequency. We observe that this cost is significantly smaller than the frequency-dependent cost naively estimated from an isolated action potential, the gap increasing with increasing frequencies. Our results demonstrate that metabolic efficiency cannot be considered only in terms of isolated action potentials but must be studied over a range of meaningful frequencies.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
Pages105-112
Number of pages8
Volume7552 LNCS
EditionPART 1
DOIs
StatePublished - 2012
Externally publishedYes
Event22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne, Switzerland
Duration: Sep 11 2012Sep 14 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7552 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd International Conference on Artificial Neural Networks, ICANN 2012
CountrySwitzerland
CityLausanne
Period09/11/1209/14/12

Keywords

  • action potential
  • energetic efficiency
  • energy metabolism
  • frequency dependence
  • Hodgkin-Huxley-type models

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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