Literature DB >> 21517530

Energy and information in Hodgkin-Huxley neurons.

A Moujahid1, A d'Anjou, F J Torrealdea, F Torrealdea.   

Abstract

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.

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Year:  2011        PMID: 21517530     DOI: 10.1103/PhysRevE.83.031912

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  20 in total

1.  Effects of channel blocking on information transmission and energy efficiency in squid giant axons.

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Journal:  J Comput Neurosci       Date:  2018-01-11       Impact factor: 1.621

2.  Spike timing precision of neuronal circuits.

Authors:  Deniz Kilinc; Alper Demir
Journal:  J Comput Neurosci       Date:  2018-04-17       Impact factor: 1.621

3.  Metabolic efficiency with fast spiking in the squid axon.

Authors:  Abdelmalik Moujahid; Alicia d'Anjou
Journal:  Front Comput Neurosci       Date:  2012-11-15       Impact factor: 2.380

4.  Input-output relation and energy efficiency in the neuron with different spike threshold dynamics.

Authors:  Guo-Sheng Yi; Jiang Wang; Kai-Ming Tsang; Xi-Le Wei; Bin Deng
Journal:  Front Comput Neurosci       Date:  2015-05-27       Impact factor: 2.380

5.  Optimum neural tuning curves for information efficiency with rate coding and finite-time window.

Authors:  Fang Han; Zhijie Wang; Hong Fan; Xiaojuan Sun
Journal:  Front Comput Neurosci       Date:  2015-06-03       Impact factor: 2.380

6.  Energy demands of diverse spiking cells from the neocortex, hippocampus, and thalamus.

Authors:  Abdelmalik Moujahid; Alicia D'Anjou; Manuel Graña
Journal:  Front Comput Neurosci       Date:  2014-04-08       Impact factor: 2.380

7.  Cable energy function of cortical axons.

Authors:  Huiwen Ju; Michael L Hines; Yuguo Yu
Journal:  Sci Rep       Date:  2016-07-21       Impact factor: 4.379

8.  Metabolic Energy of Action Potentials Modulated by Spike Frequency Adaptation.

Authors:  Guo-Sheng Yi; Jiang Wang; Hui-Yan Li; Xi-Le Wei; Bin Deng
Journal:  Front Neurosci       Date:  2016-11-17       Impact factor: 4.677

Review 9.  Can the activities of the large scale cortical network be expressed by neural energy? A brief review.

Authors:  Rubin Wang; Yating Zhu
Journal:  Cogn Neurodyn       Date:  2015-09-03       Impact factor: 5.082

10.  Dynamic transition of neuronal firing induced by abnormal astrocytic glutamate oscillation.

Authors:  Jiajia Li; Jun Tang; Jun Ma; Mengmeng Du; Rong Wang; Ying Wu
Journal:  Sci Rep       Date:  2016-08-30       Impact factor: 4.379

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