Dynamics and coherence resonance in a thermosensitive neuron driven by photocurrent
Ying Xu(徐莹)1,2, Minghua Liu(刘明华)3,1, Zhigang Zhu(朱志刚)1, Jun Ma(马军)1,4
1 Department of Physics, Lanzhou University of Technology, Lanzhou 730050, China;
2 Department of Physics, Central China Normal University, Wuhan 430079, China;
3 Electrical Engineering College, Northwest Minzu University, Lanzhou 730124, China;
4 School of Science, Chongqing University of Posts and Telecommunications, Chongqing 430065, China
A feasible neuron model can be effective to estimate the mode transition in neural activities in a complex electromagnetic environment. When neurons are exposed to electromagnetic field, the continuous magnetization and polarization can generate nonlinear effect on the exchange and propagation of ions in the cell, and then the firing patterns can be regulated completely. The conductivity of ion channels can be affected by the temperature and the channel current is adjusted for regulating the excitability of neurons. In this paper, a phototube and a thermistor are used to the functions of neural circuit. The phototube is used to capture external illumination for energy injection, and a continuous signal source is obtained. The thermistor is used to percept the changes of temperature, and the channel current is changed to adjust the excitability of neuron. This functional neural circuit can encode the external heat (temperature) and illumination excitation, and the dynamics of neural activities is investigated in detail. The photocurrent generated in the phototube can be used as a signal source for the neural circuit, and the thermistor is used to estimate the conduction dependence on the temperature for neurons under heat effect. Bifurcation analysis and Hamilton energy are calculated to explore the mode selection. It is found that complete dynamical properties of biological neurons can be reproduced in spiking, bursting, and chaotic firing when the phototube is activated as voltage source. The functional neural circuit mainly presents spiking states when the photocurrent is handled as a stable current source. Gaussian white noise is imposed to detect the occurrence of coherence resonance. This neural circuit can provide possible guidance for investigating dynamics of neural networks and potential application in designing sensitive sensors.
Project supported by the National Natural Science Foundation of China (Grant No. 11672122).
Corresponding Authors:
Jun Ma
E-mail: hyperchaos@lut.edu.cn,hyperchaos@163.com
Cite this article:
Ying Xu(徐莹), Minghua Liu(刘明华), Zhigang Zhu(朱志刚), Jun Ma(马军) Dynamics and coherence resonance in a thermosensitive neuron driven by photocurrent 2020 Chin. Phys. B 29 098704
[1]
Schwiening C J 2012 J. Physiol. 590 2571
[2]
Hindmarsh J L and Rose R M 1984 Proc. R. Soc. B Biol. Sci. 221 87
[3]
Jia B, Gu H G and Xue L 2017 Cogn. Neurodyn. 11 189
[4]
Zhu F, Wang R, Pan X, et al. 2019 Cogn. Neurodyn. 13 75
[5]
Wu F, Ma J, Zhang G, et al. 2019 Appl. Math. Comput. 347 590
[6]
Wu F, Ma J, Zhang G, et al. 2020 Sci. Chin. Technol. Sci. 63 625
[7]
Cunningham M O, Whittington M A, Bibbig A, et al. 2004 Proc. Natl. Acad. Sci. USA 101 7152
[8]
Wu J and Ma S 2019 Nonlin. Dyn. 96 1895
[9]
Han X, Bi Q, Ji P, et al. 2015 Phys. Rev. E 92 012911
[10]
Han X, Liu Y, Bi Q, et al. 2019 Commun. Nonlin. Sci. Numer. Simulat. 72 16
[11]
Allen N J and Eroglu C 2017 Neuron 96 697
[12]
Chung W, Allen N J, Eroglu C, et al. 2015 CSH Perspect. Biol. 7 a020370
[13]
Vasile F, Dossi E, Rouach N, et al. 2017 Brain Struct. Funct. 222 2017
[14]
Huguet G, Joglekar A, Messi L M, et al. 2016 Biophys. J. 111 452
[15]
Tang J, Zhang J, Ma J, et al. 2017 Sci. Chin. Technol. Sci. 60 1011
[16]
Guo S, Tang J, Ma J, et al. 2017 Complexity 2017 4631602
[17]
Wang C, Guo S, Xu Y, et al. 2017 Complexity 2017 5436737
[18]
Bekkers J M 2003 Curr. Biol. 13 R433
[19]
Yue Y, Liu L, Liu Y, et al. 2017 Nonlin. Dyn. 90 2893
[20]
Uzun R, Yilmaz E, Ozer M, et al. 2017 Physica A 486 386
[21]
Yilmaz E, Baysal V, Ozer M, et al. 2016 Physica A 444 538
[22]
Yang X L, Yu Y H and Sun Z K 2017 Chaos 27 083117
[23]
Gong Y B, Wang B Y and Xie H J 2016 Biosyst. 150 132
[24]
Song X L, Wang C N, Ma J, et al. 2015 Sci. Chin. Technol. Sci. 58 1007
[25]
Song X, Wang H, Chen Y, et al. 2019 Nonlin. Dyn. 96 2341
[26]
Song X, Wang H, Chen Y, et al. 2018 Nonlin. Dyn. 94 141
[27]
Zhao Z and Gu H 2017 Sci. Rep. 7 6760
[28]
Kim Y, Park J and Choi Y K 2019 Antioxidants 8 121
[29]
Wang C and Ma J 2018 Int. J. Mod. Phys. B 32 1830003
[30]
Ma J, Yang Z, Yang L, et al. 2019 J. Zhejiang Univ. Sci. A 20 639
[31]
Ma J and Tang J 2015 Sci. Chin. Technol. Sci. 58 2038
[32]
Chua L O 2011 Appl. Phys. A 102 765
[33]
Wang Z, Joshi S, Savelev S, et al. 2017 Nat. Mater. 16 101
[34]
Muthuswamy B 2010 Int. J. Bifur. Chaos 20 1335
[35]
Kim H, Sah M P, Yang C, et al. 2012 IEEE Tr. Circ. Syst. 59 2422
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