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Cooperative activation of sodium channels for downgrading the energy efficiency in neuronal information processing |
Haoran Yan(严浩然)1, Jiaqi Yan(颜家琦)1, Lianchun Yu(俞连春)1,2,†, and Yu-Feng Shao(邵玉峰)3 |
1 School of Physical Science and Technology, Lanzhou Center for Theoretical Physics, Lanzhou University, Lanzhou 730000, China; 2 Key Laboratory of Theoretical Physics of Gansu Province, and Key Laboratory of Quantum Theory and Applications of MoE, Lanzhou University, Lanzhou 730000, China; 3 Department of Neuroscience, Anatomy, Histology, and Embryology, Key Laboratory of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China |
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Abstract The Hodgkin-Huxley model assumes independent ion channel activation, although mutual interactions are common in biological systems. This raises the problem why neurons would favor independent over cooperative channel activation. In this study, we evaluate how cooperative activation of sodium channels affects the neuron's information processing and energy consumption. Simulations of the stochastic Hodgkin-Huxley model with cooperative activation of sodium channels show that, while cooperative activation enhances neuronal information processing capacity, it greatly increases the neuron's energy consumption. As a result, cooperative activation of sodium channel degrades the energy efficiency for neuronal information processing. This discovery improves our understanding of the design principles for neural systems, and may provide insights into future designs of the neuromorphic computing devices as well as systematic understanding of pathological mechanisms for neural diseases.
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Received: 27 November 2023
Revised: 15 January 2024
Accepted manuscript online: 24 January 2024
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PACS:
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88.05.Bc
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(Energy efficiency; definitions and standards)
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87.16.Vy
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(Ion channels)
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87.19.lb
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(Action potential propagation and axons)
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87.19.lj
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(Neuronal network dynamics)
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Fund: This study was supported by the Fundamental Research Funds for the Central Universities (Grant No. lzujbky-2021- 62), the Shanghai Municipal Science and Technology Major Project (Grant No. 2018SHZDZX01), Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (LCNBI) and ZJLab, and the National Natural Science Foundation of China (Grant No. 12247101). |
Corresponding Authors:
Lianchun Yu
E-mail: yulch@lzu.edu.cn
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Cite this article:
Haoran Yan(严浩然), Jiaqi Yan(颜家琦), Lianchun Yu(俞连春), and Yu-Feng Shao(邵玉峰) Cooperative activation of sodium channels for downgrading the energy efficiency in neuronal information processing 2024 Chin. Phys. B 33 058801
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[1] Kandel E, Schwartz J, Jessell T, Siegelbaum S and Hudspeth A J 2013 Principles of Neural Science 5nd edn. (New York: McGraw-Hill Medical) [2] Hodgkin A L and Huxley A F 1952 J. Physiol. 117 500 [3] Dayan P and Abbott L F 2001 Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Cambridge: MIT Press) [4] Fox R F 1997 Biophys. J. 72 2608 [5] Chow C C and White J A 1996 Biophys. J. 71 3013 [6] Naundorf B, Wolf F and Volgushev M 2006 Nature 440 1060 [7] Yu Y, Shu Y and McCormick D A 2008 J. Neurosci. 28 7260 [8] McCormick D A, Shu Y and Yu Y 2007 Nature 445 E1 [9] Yu L and Yu Y 2017 J. Neurosci. Res. 95 2253 [10] Ding Q, Wu Y, Li T, Yu D and Jia Y 2023 Chaos Solitons Fract. 171 113464 [11] Lu L, Yi M, Gao Z, Wu Y and Zhao X 2023 Nonlinear Dyn. 111 16557 [12] Zeng S and Jung P 2004 Phys. Rev. E 70 011903 [13] Moujahid A, d’Anjou A, Torrealdea F J and Torrealdea F 2011 Phys. Rev. E 83 031912 [14] Ma J 2023 J. Zhejiang Univ. Sci. A 24 109 [15] Zhu F, Wang R, Pan X and Zhu Z 2019 Cogn. Neurodyn. 13 75 [16] Li X, Yu D, Li T and Jia Y 2023 Nonlinear Dyn 112 2933 [17] Yu L and Liu L 2014 Phys. Rev. E 89 032725 [18] Fu Y M, Wan C J, Zhu L Q, Xiao H and Wan Q 2018 Adv. Biosyst. 2 1700198 [19] Liu Z, Han F and Wang Q 2022 Nonlinear Dyn. 108 1849 [20] Yu Y, Fan Y, Han F, Luan G and Wang Q 2023 Sci. China Technol. Sci. 66 3628 [21] Boucher P A, Joós B and Morris C E 2012 J. Comput. Neurosci. 33 301
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