Special Issue:
SPECIAL TOPIC — 80th Anniversary of Northwestern Polytechnical University (NPU)
|
SPECIAL TOPIC—80th Anniversary of Northwestern Polytechnical University (NPU) |
Prev
Next
|
|
|
Effect of stochastic electromagnetic disturbances on autapse neuronal systems |
Liang-Hui Qu(曲良辉)1,3, Lin Du(都琳)1, Zi-Chen Deng(邓子辰)1,2, Zi-Lu Cao(曹子露)1, Hai-Wei Hu(胡海威)1 |
1 School of Natural and Applied Sciences, Northwestern Polytechnical University, Xi'an 710129, China;
2 School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi'an 710129, China;
3 College of Science, Zhongyuan University of Technology, Zhengzhou 450007, China |
|
|
Abstract With the help of a magnetic flux variable, the effects of stochastic electromagnetic disturbances on autapse Hodgkin-Huxley neuronal systems are studied systematically. Firstly, owing to the autaptic function, the inter-spike interval series of an autapse neuron not only bifurcates, but also presents a quasi-periodic characteristic. Secondly, an irregular mixed-mode oscillation induced by a specific electromagnetic disturbance is analyzed using the coefficient of variation of inter-spike intervals. It is shown that the neuronal discharge activity has certain selectivity to the noise intensity, and the appropriate noise intensity can induce the significant mixed-mode oscillations. Finally, the modulation effects of electromagnetic disturbances on a ring field-coupled neuronal network with autaptic structures are explored quantitatively using the average spiking frequency and the average coefficient of variation. The electromagnetic disturbances can not only destroy the continuous and synchronous discharge state, but also induce the resting neurons to generate the intermittent discharge mode and realize the transmission of neural signals in the neuronal network. The studies can provide some theoretical guidance for applying electromagnetic disturbances to effectively control the propagation of neural signals and treat mental illness.
|
Received: 13 July 2018
Revised: 28 September 2018
Accepted manuscript online:
|
PACS:
|
87.19.ll
|
(Models of single neurons and networks)
|
|
87.18.Sn
|
(Neural networks and synaptic communication)
|
|
87.19.lc
|
(Noise in the nervous system)
|
|
05.40.Ca
|
(Noise)
|
|
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 11672233), the Fundamental Research Funds for the Central Universities, China (Grant No. 3102017AX008), and the Seed Foundation of Innovation and Creation for Graduate Student in Northwestern Polytechnical University, China (Grant No. ZZ2018173). |
Corresponding Authors:
Lin Du
E-mail: lindu@nwpu.edu.cn
|
Cite this article:
Liang-Hui Qu(曲良辉), Lin Du(都琳), Zi-Chen Deng(邓子辰), Zi-Lu Cao(曹子露), Hai-Wei Hu(胡海威) Effect of stochastic electromagnetic disturbances on autapse neuronal systems 2018 Chin. Phys. B 27 118707
|
[1] |
Hodgkin A L and Huxley A F 1952 J. Physiol. 117 500
|
[2] |
Fitzhugh R 1961 Biophys J. 1 445
|
[3] |
Morris C and Lecar H 1981 Biophys J. 35 193
|
[4] |
Hindmarsh J L and Rose R M 1984 Proc. R. Soc. Lond. B, Biol. Sci. 221 87
|
[5] |
Shi X and Zhang J D 2016 Chin. Phys. B 25 060502
|
[6] |
Izhikevich E M 2004 IEEE Trans. Neural Netw. 15 1063
|
[7] |
Xu L F, Li C D and Chen L 2016 Acta Phys. Sin. 65 240701(in Chinese)
|
[8] |
Walsh V and Cowey A 2000 Nat. Rev. Neurosci. 1 73
|
[9] |
Rabinovich M I, Varona P, Selverston A I and Abarbanel H D I 2006 Rev. Mod. Phys. 78 1213
|
[10] |
Lu Q S, Liu S Q, Liu F, Wang Q Y, Hou Z H and Zheng Y H 2008 Adv. Mech. 38 766(in Chinese)
|
[11] |
Yi G S, Wang J, Wei X L, Deng B, Li H Y and Han C X 2014 Appl. Math. Comput. 231 100
|
[12] |
Gu H G, Chen S G and Li Y Y 2015 Chin. Phys. B 24 050505
|
[13] |
Bekkers J M 2003 Curr. Biol. 13 433
|
[14] |
Wang H T and Chen Y 2015 Chin. Phys. B 24 128709
|
[15] |
Li Y Y, Schmid G, Hänggi P and Schimansky-Geier L 2010 Phys. Rev. E 82 061907
|
[16] |
Hashemi M, Valizadeh A and Azizi Y 2012 Phys. Rev. E 85 021917
|
[17] |
Yang X L, Yu Y H and Sun Z K 2017 Chaos 27 083117
|
[18] |
Guo S L, Tang J, Ma J and Wang C N 2017 Complexity 2017 4631602
|
[19] |
Ma J, Song X L Tang J and Wang C N 2015 Neurocomputing 167 378
|
[20] |
Ergin Y, Veli B, Matjaž P and Mahmut O 2016 Sci. China Tech. Sci. 59 364
|
[21] |
Lübke J, Markram H M and Sakmann B 1996 J. Neurosci. 16 3209
|
[22] |
Yu W T, Zhang J and Tang J 2017 Acta Phys. Sin. 66 200201(in Chinese)
|
[23] |
Li J J, Wu Y, Du M M and Liu W M 2015 Acta Phys. Sin. 64 030503(in Chinese)
|
[24] |
Lv M and Ma J 2016 Neurocomputing 205 375
|
[25] |
Lv M, Wang C N, Ren G D, Ma J and Song X L 2016 Nonlinear Dynam. 85 1479
|
[26] |
Ma J, Wu F Q, Hayat T, Zhou P and Tang J 2017 Physica A 486 508
|
[27] |
Wang Y, Ma J, Xu Y, Wu F Q and Zhou P 2017 Int. J. Bifurcat. Chaos 27 1750030
|
[28] |
Wu J, Xu Y and Ma J 2017 PLoS One 12 e0174330
|
[29] |
Ma J and Tang J 2017 Nonlinear Dynam. 89 1569
|
[30] |
Honeycutt R L 1992 Phys. Rev. A 45 600
|
[31] |
Yue Y, Liu L W, Liu Y J, Chen Y, Chen Y L and Yu L C 2017 Nonlinear Dynam. 90 2893
|
[32] |
Gu H G and Xiao W W 2014 Int. J. Bifurcat. Chaos 24 1450082
|
[33] |
Drover J, Rubin J, Su J Z and Ermentrout B 2004 Siam J. Appl. Math. 65 69
|
[34] |
Doi S, Inoue J and Kumagai S 2004 J. Integr. Neurosci. 3 207
|
[35] |
Rubin J and Wechselberger M 2007 Biol. Cybern. 97 5
|
[36] |
Borowski P, Kuske R, Li Y X and Cabrera J L 2010 Chaos 20 043117
|
[37] |
Yue Y, Liu Y J, Song Y L, Chen Y and Yu L C 2017 Chin. Phys. Lett. 34 048701
|
[38] |
Ergin Y, Mahmut O, Veli B and Matjaž P 2016 Sci. Rep-UK 6 30914
|
[39] |
Du L, Yang Y and Lei Y M 2018 Appl. Math. Mech.-Engl. Ed. 39 353
|
[40] |
Du L, Zhao Y P, Lei Y M, Hu J and Yue X L 2018 Nonlinear Dynam. 92 1921
|
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
|
|
|