|
|
Dynamics and synchronization of neural models with memristive membranes under energy coupling |
Jingyue Wan(万婧玥)1, Fuqiang Wu(吴富强)1,2,†, Jun Ma(马军)3, and Wenshuai Wang(汪文帅)1,2 |
1 School of Mathematics and Statistics, Ningxia University, Yinchuan 750021, China; 2 Ningxia Basic Science Research Center of Mathematics, Ningxia University, Yinchuan 750021, China; 3 Department of Physics, Lanzhou University of Technology, Lanzhou 730050, China |
|
|
Abstract Dynamical modeling of neural systems plays an important role in explaining and predicting some features of biophysical mechanisms. The electrophysiological environment inside and outside of the nerve cell is different. Due to the continuous and periodical properties of electromagnetic fields in the cell during its operation, electronic components involving two capacitors and a memristor are effective in mimicking these physical features. In this paper, a neural circuit is reconstructed by two capacitors connected by a memristor with periodical mem-conductance. It is found that the memristive neural circuit can present abundant firing patterns without stimulus. The Hamilton energy function is deduced using the Helmholtz theorem. Further, a neuronal network consisting of memristive neurons is proposed by introducing energy coupling. The controllability and flexibility of parameters give the model the ability to describe the dynamics and synchronization behavior of the system.
|
Received: 06 December 2023
Revised: 29 December 2023
Accepted manuscript online: 12 January 2024
|
PACS:
|
05.45.Pq
|
(Numerical simulations of chaotic systems)
|
|
05.45.Xt
|
(Synchronization; coupled oscillators)
|
|
05.45.Gg
|
(Control of chaos, applications of chaos)
|
|
Fund: This study was funded by the National Natural Science Foundation of China (Grant No. 12302070) and the Ningxia Science and Technology Leading Talent Training Program (Grant No. 2022GKLRLX04). |
Corresponding Authors:
Fuqiang Wu
E-mail: alexwutian@nxu.edu.cn
|
Cite this article:
Jingyue Wan(万婧玥), Fuqiang Wu(吴富强), Jun Ma(马军), and Wenshuai Wang(汪文帅) Dynamics and synchronization of neural models with memristive membranes under energy coupling 2024 Chin. Phys. B 33 050504
|
[1] Friston K 2010 Nat. Rev. Neurosci. 11 127 [2] Moujahid A, D’Anjou A, Torrealdea F J and Torrealdea F 2011 Phys. Rev. E 83 031912 [3] Li S and Sheng Z H 2022 Nat. Rev. Neurosci. 23 4 [4] Nowotny T and Rabinovich M I 2007 Phys. Rev. Lett. 98 128106 [5] Canavier C C, Clark J W and Byrne J H 1990 Biophys. J. 57 1245 [6] Rabinovich M I, Varona P, Selverston A I and Abarbanel H D I 2006 Rev. Mod. Phys. 78 1213 [7] Hodgkin A L and Huxley A F 1952 J. Physiol. 117 500 [8] Chua L, Sbitnev V and Kim H 2012 Int. J. Bifurcat. Chaos 22 1230011 [9] Sah M P, Kim H and Chua L O 2014 IEEE Circ. Syst. Mag. 14 12 [10] Lv M, Wang C N, Ren G D, Ma J and Song X L 2016 Nonlinear Dyn. 85 1479 [11] Lv M and Ma J 2016 Neurocomputing 205 375 [12] Bao H, Hu A H, Liu W B and Bao B C 2020 IEEE T. Neur. Net. Lear. 31 502 [13] Ding S, Wang N, Bao H, Chen B, Wu H and Xu Q 2023 Chaos Soliton. Fract. 166 112899 [14] Xie Y, Yao Z, Hu X K and Ma J 2021 Chin. Phys. B 30 120510 [15] Zhang X F and Ma J 2021 Journal of Zhejiang University: Science A 22 707 [16] Shen H, Yu F, Wang C H, Sun J R and Cai S 2022 Nonlinear Dyn. 110 3807 [17] Wu F Q and Yao Z 2023 Nonlinear Dyn. 111 13481 [18] Wu F Q, Guo Y T, Ma J and Jin W Y 2023 Appl. Math. Comput. 455 128131 [19] Wu F Q, Guo Y T and Ma J 2022 Nonlinear Dyn. 109 2063 [20] Zhang Y, Xu Y, Yao Z and Ma J 2020 Nonlinear Dyn. 102 1849 [21] Wu F Q, Hu X K and Ma J 2022 Appl. Math. Comput. 432 127366 [22] Guo Y, Wu F, Yang F and Ma J 2023 Chaos 33 113106 [23] Xu Q, Ju Z, Ding S, Feng C, Chen M and Bao B C 2022 Cogn. Neurodyn. 16 1221 [24] Zhang H, Wang L, Zhang P, Zhang X F and Ma J 2021 Chin. Phys. B 30 038702 [25] Qiao S, Gao C H and An X L 2023 Nonlinear Dyn. 111 10529 [26] An X L, Xiong L, Shi Q, Qiao S and Zhang L 2023 Nonlinear Dyn. 111 9509 [27] Ma J and Tang J 2015 Sci. China Technol. Sc. 58 2038 [28] Yang F F, Wang Y and Ma J 2023 Commun. Nonlinear Sci. 119 107127 [29] Xie Y, Yao Z and Ma J 2022 Front. Inform. Technol. Electron. Eng. 23 1407 [30] Ma J 2024 Appl. Math. Comput. 463 128379 [31] Xie Y, Zhou P and Ma J 2023 Appl. Math. Model. 113 175 [32] Xie Y, Zhou P, Yao Z and Ma J 2022 Physica A 607 128175 [33] Torrealdea F J, D’Anjou A, Graña M and Sarasola C 2006 Phys. Rev. E 74 011905 [34] Liu Z L, Yu Y and Wang Q Y 2022 Sci. China Technol. Sc. 65 1435 [35] Liu Z, Han F and Wang Q Y 2022 Nonlinear Dyn. 108 1849 [36] Torrealdea F J, Sarasola C and d’Anjou A 2009 Chaos Soliton. Fract. 40 60 [37] Wu F Q and Wang R B 2023 Commun. Nonlinear Sci. 126 107459 [38] Zhou P, Zhang X F and Ma J 2022 Nonlinear Dyn. 108 1681 [39] Wu F Q, Ma J and Zhang G 2020 Sci. China Technol. Sc. 63 625 [40] Xie Y, Xu Y and Ma J 2023 Nonlinear Dyn. 111 11521 [41] Xie Y and Ma J 2022 J. Biol. Phys. 48 339 [42] Xie Y, Yao Z and Ma J 2023 Sci. China Technol. Sc. 66 439 [43] Yang Z, Zhang Y and Wu F Q 2020 Nonlinear Dyn. 100 647 [44] Usha K and Subha P A 2019 Chin. Phys. B 28 020502 [45] Malik S A and Mir A H 2020 Neural Networks 123 372 [46] Herz A V M, Gollisch T, Machens C K and Jaeger D 2006 Science 314 80 [47] Wu F Q, Meng H and Ma J 2024 Neural Networks 169 607 [48] Barry J F, Turner M J, Schloss J M, Glenn D R, Song Y, Lukin M D, Park H and Walsworth R L 2016 Proc. Natl. Acad. Sci. USA 113 14133 [49] Chua L O 1971 IEEE T. Circ. Theory 18 507 [50] Song X L, Jin W Y and Ma J 2015 Chin. Phys. B 24 128710 [51] Sarasola C, Torrealdea F J, D’Anjou A, Moujahid A and Graña M 2004 Phys. Rev. E 69 011606 [52] Heitmann S, Aburn M J and Breakspear M 2018 Neurocomputing 315 82 |
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
|
|
|