INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
Prev
Next
|
|
|
Effects of electric field on vibrational resonances in Hindmarsh-Rose neuronal systems for signal detection |
Xiaoxia Li(李晓霞)1,3,†, Xiaopeng Xue(薛小鹏)2, Dongjie Liu(刘栋杰)1,3, Tianyi Yu(余天意)1,3, Qianqian He(何倩倩)2, and Guizhi Xu(徐桂芝)1,2,3 |
1 State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; 2 School of Health Sciences&Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China; 3 Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China |
|
|
Abstract Changes in the concentration of charged ions in neurons can generate induced electric fields, which can further modulate cell membrane potential. In this paper, Fourier coefficients are used to investigate the effect of electric field on vibrational resonance for signal detection in a single neuron model and a bidirectionally coupled neuron model, respectively. The study found that the internal electric field weakens vibrational resonance by changing two factors, membrane potential and phase-locked mode, while the periodic external electric field of an appropriate frequency significantly enhances the vibrational resonance, suggesting that the external electric field may play a constructive role in the detection of weak signals in the brain and neuronal systems. Furthermore, when the coupling of two neurons is considered, the effect of the electric field on the vibrational resonance is similar to that of a single neuron. The paper also illustrates the effect of electric field coupling on vibrational resonance. This study may provide a new theoretical basis for understanding information encoding and transmission in neurons.
|
Received: 16 August 2022
Revised: 29 September 2022
Accepted manuscript online: 21 October 2022
|
PACS:
|
87.19.ll
|
(Models of single neurons and networks)
|
|
87.18.Sn
|
(Neural networks and synaptic communication)
|
|
87.19.ln
|
(Oscillations and resonance)
|
|
87.19.lg
|
(Synapses: chemical and electrical (gap junctions))
|
|
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 51737003 and 51977060) and the Natural Science Foundation of Hebei Province, China (Grant No. E2011202051). |
Corresponding Authors:
Xiaoxia Li
E-mail: lixiaoxia@hebut.edu.cn
|
Cite this article:
Xiaoxia Li(李晓霞), Xiaopeng Xue(薛小鹏), Dongjie Liu(刘栋杰), Tianyi Yu(余天意), Qianqian He(何倩倩), and Guizhi Xu(徐桂芝) Effects of electric field on vibrational resonances in Hindmarsh-Rose neuronal systems for signal detection 2023 Chin. Phys. B 32 048701
|
[1] Benzi R, Sutera A and Vulpiani A 1981 Physica A 14 453 [2] Benzi R, Parisi G, Sutera A and Vulpiani A 1982 Tellus 34 10 [3] Benzi R 2010 Nonlinear Process Geophys 17 431 [4] Mikhaylov A N, Guseinov D V, Belov A I, et al. 2021 Chaos, Solitons and Fractals 144 110723 [5] Bai C, Du L and Mei D 2009 Cent. Eur. J. Phys. 7 601 [6] Wang C J 2004 Phys. Scr. 80 065004 [7] Han Q, Yang T and Zeng C, Wang H, Liu Z, Fu Y, Zhang C and Tian D 2014 Physica A 408 96 [8] Zhang R F, Cheng Q H and Xu D H 2015 Acta Phys. Sin 64 024211 (in Chinese) [9] Chizhevsky V N and Giacomelli G 2006 Phys. Rev. E 73 022103 [10] Hänggi P 2002 Chempluschem 3 285 [11] Landa P S and McClintock P V E 2000 J. Phys. A: Math. Theor. 33 L433 [12] Zaikin A A, López L, Baltanás J P, Kurths J and Sanjuán M A F 2002 Phys. Rev. E 66 011106 [13] Chizhevsky V N, Smeu E and Giacomelli G 2003 Phys. Rev. Lett. 91 220602 [14] Ghosh S and Ray D S 2013 Phys. Rev. E 88 042904 [15] Carroll T L and Pecora L M 1993 Phys. Rev. Lett. 70 576 [16] Nobukawa S and Shibata N 2019 Sci. Rep. 9 4990 [17] Baysal V, Saraç Z and Yilmaz E 2019 Nonlinear Dyn. 97 1275 [18] Yao Y, Ma J, Gui R and Cheng G 2021 Chaos 31 023103 [19] González-Miranda J M 2007 Int. J. Bifur. Chaos 17 3071 [20] Hodgkin A L and Huxley A F 1952 J. Physiol. 117 500 [21] Noble D 1960 Nature 188 495 [22] Chua L, Sbitnev V and Kim H 2012 Int. J. Bifur. Chaos 22 1230011 [23] Hu X and Liu C 2019 Nonlinear Dyn. 97 1721 [24] FitzHugh R 1969 Bioeng 1 85 [25] Morris C and Lecar H 1981 Biophys. J. 35 193 [26] Hindmarsh J L and Rose R M 1982 Nature 296 162 [27] Prescott S A, Ratté S, De Koninck Y and Sejnowski T J 2006 J. Neurosci. 26 9084 [28] Wiesenfeld K and Jaramillo F 1998 Chaos 8 539 [29] Shepherd G M 2004 OUP [30] Song X, Wang H and Chen Y 2018 Nonlinear Dyn. 94 141 [31] Han C, Qin Y and Qin Q 2019 Physica A 523 204 [32] Li H, Sun X and Xiao J 2018 Chaos 28 043113 [33] Douglass J K, Wilkens L, Pantazelou E and Moss F 1993 Nature 365 337 [34] Lv M, Wang C, Ren G, Ma J and Song X 2016 Nonlinear Dyn. 85 1479 [35] Bhargavan M 2008 Health. Phys. 95 612 [36] Shneider M N and Pekker M 2013 J. Appl. Phys. 114 104701 [37] Oberschleissheim 2020 Health. Phys. 118 483 [38] Capelli E, Torrisi F, Venturini L, Granato M, Fassina L, Lupo G F D and Ricevuti G 2017 J. Healthc. Eng. 2017 2530270 [39] Ahmad R H M A, Fakhoury M and Lawand N 2020 Curr. Alzheimer. Res. 17 1001 [40] Fisher R, Salanova V, Witt T, et al. 2010 Epilepsia 51 899 [41] Muñana K R 2013 Top. Companion. Anim. M 28 67 [42] Yao Y, Su C and Xiong J 2019 Physica A 531 121734 [43] Ge M, Lu L, Xu Y, Mamatimin R, Pei Q and Jia Y 2020 Chaos, Solitons and Fractals 133 109645 [44] Baysal V and Yilmaz E 2020 Physica A 537 122733 [45] Ma J, Zhang G, Hayat T and Ren G 2019 Nonlinear Dyn 95 1585 [46] Hou Z, Ma J, Zhan X, Yang L and Jia Y 2021 Chaos, Solitons and Fractals 142 110522 [47] Rubin J E and Terman D 2004 J. Comput. Neurosci. 16 211 [48] Sanders T H 2017 Front. Integr. Neurosci. 11 24 [49] Stefani A, Trendafilov V, Liguori C, Fedele E and Galatib S 2017 Prog. Neurobiol. 151 157 [50] Wouapi K, Fotsin B H, Louodop F P, Feudjio K F, Njitacke Z T and Hermann Djeudjo T 2020 Cogn. Neurodyn. 14 375 [51] Deng B, Wang J and Wei X 2009 Chaos 19 013117 [52] Wu X X, Yao C and Shuai J 2015 Sci. Rep. 5 1 [53] Merrill D R, Bikson M and Jefferys J G R 2005 J. Neurosci. Methods 141 171 [54] Lv M, Ma J, Yao Y G and Alzahrani F 2019 Sci China Technol. Sci. 62 448 [55] Xu Y, Jia Y, Ma J, Hayat T and Alsaedi A 2018 Sci. Rep. 8 1 [56] Ma J, Wu F, Alsaedi A and Tang J 2018 Nonlinear Dyn. 93 2057 [57] Lozano A M, Lipsman N, Bergman H, Brown P, Chabardes S, Chang J W, Matthews K, McIntyre C C, Schlaepfer T E, Schulder M, Temel Y, Volkmann J and Krauss J K 2019 Nat. Rev. Neurol. 15 148 [58] Lozano A M and Lipsman N 2013 Neuron 77 406 [59] Ashkan K, Rogers P, Bergman H and Ughratdar I 2017 Nat. Rev. Neurol. 13 548 [60] Groome J R 2014 Voltage Gated Sodium Channels pp. 7-31 |
No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|