Please wait a minute...
Chin. Phys. B, 2021, Vol. 30(12): 120510    DOI: 10.1088/1674-1056/ac1fdc
Special Issue: SPECIAL TOPIC— Interdisciplinary physics: Complex network dynamics and emerging technologies
SPECIAL TOPIC—Interdisciplinary physics: Complex network dynamics and emerging technologies Prev   Next  

Enhance sensitivity to illumination and synchronization in light-dependent neurons

Ying Xie(谢盈)1, Zhao Yao(姚昭)1, Xikui Hu(胡锡奎)2, and Jun Ma(马军)1,2,†
1 Department of Physics, Lanzhou University of Technology, Lanzhou 730050, China;
2 School of Science, Chongqing University of Posts and Telecommunications, Chongqing 430065, China
Abstract  When a phototube is activated to connect a neural circuit, the output voltage becomes sensitive to external illumination because the photocurrent across the phototube can be controlled by external electromagnetic wave. The channel currents from different branch circuits have different impacts on the outputs voltage of the neural circuit. In this paper, a phototube is incorporated into different branch circuits in a simple neural circuit, and then a light-controlled neuron is obtained for further nonlinear analysis. Indeed, the phototube is considered as exciting source when it is activated by external illumination, and two kinds of light-sensitive neurons are obtained when the phototube is connected to capacitor or induction coil, respectively. Electric synapse coupling is applied to detect possible synchronization between two functional neurons, and the energy consumption along the coupling channel via resistor is estimated. The analog circuits for the two kinds of light-sensitive neurons are supplied for further confirmation by using Multisim. It is found that two light-sensitive neurons and neural circuits can be synchronized by taming the coupling intensity carefully. It provides possible clues to understand the synchronization mechanism for eyes and artificial sensors which are sensitive to illumination. Finally, a section for open problems is supplied for further investigation about its collective behaviors in the network with/without synapse coupling.
Keywords:  light-sensitive neuron      synchronization      energy consumption      bifurcation      neural circuit  
Received:  14 July 2021      Revised:  09 August 2021      Accepted manuscript online:  22 August 2021
PACS:  05.45.-a (Nonlinear dynamics and chaos)  
  87.18.Hf (Spatiotemporal pattern formation in cellular populations)  
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 12062009).
Corresponding Authors:  Jun Ma     E-mail:

Cite this article: 

Ying Xie(谢盈), Zhao Yao(姚昭), Xikui Hu(胡锡奎), and Jun Ma(马军) Enhance sensitivity to illumination and synchronization in light-dependent neurons 2021 Chin. Phys. B 30 120510

[1] Tang J, Zhang J, Ma J, Zhang G Y and Yang X Q 2017 Sci. China Technol. Sci. 60 1011
[2] Tabi C B, Etémé A S, Mohamadou A and Kofané T C 2019 Chaos, Solitons & Fractals 123 116
[3] Kundu S, Majhi S and Ghosh D 2019 Nonlinear Dyn. 98 1659
[4] Baysal V and Yilmaz E 2020 Physica A 537 122733
[5] Shaverdi Y, Panahi S, Kapitaniak T and Jafari S 2019 Eur. Phys. J. Spec. Top. 228 2405
[6] Ditlevsen S and Greenwood P 2013 J. Math. Biol. 67 239
[7] Mondal A, Upadhyay R K, Ma J, Yadav B K, Sharma S K and Mondal A 2019 Cogn. Neurodyn. 13 393
[8] Lin H, Wang C, Sun Y and Wang Y 2020 Nonlinear Dyn. 100 3667
[9] Shilnikov A L and Rulkov N F 2004 Phys. Lett. A 328 177
[10] Nobukawa S, Nishimura H, Yamanishi T and Liu J Q 2015 PloS One 10 e0138919
[11] Ascoli A, Slesazeck S, Mähne H, Tetzlaff R and Mikolajick T 2015 IEEE Tr. Circ. Syst. I 62 1165
[12] Kvatinsky S, Friedman E, Kolodny A and Weiser U 2013 IEEE Circ. Syst. Mag. 13 17
[13] Bao H, Chen M, Wu H G and Bao B C 2019 Sci. China Technol. Sci. 63 603
[14] Makhlin Y, Schön G and Shnirman A 2001 Rev. Mod. Phys. 73 357
[15] Sickinger H, Lipman A, Weides M, Mints R G, Kohlstedt H, Koelle D, Kleiner R and Goldobin E 2012 Phys. Rev. Lett. 109 107002
[16] Zhang Y, Zhou P, Tang J and Ma J 2021 Chin. J. Phys. 71 72
[17] Zhang Y, Xu Y, Yao Z and Ma J 2020 Nonlinear Dyn. 102 1849
[18] Zhang Y, Wang C, Tang J, Ma J and Ren G D 2020 Sci. China Technol. Sci. 63 2328
[19] Zhang H, Wang L, Zhang P, Zhang X D and Ma J 2021 Chin. Phys. B 30 038702
[20] Kafraj M S, Parastesh F and Jafari S 2020 Chaos, Solitons & Fractals 137 109782
[21] Feltz A and Pölzl W 2000 J. Euro. Cera. Soc. 20 2353
[22] Yakovleva M, Bhand S and Danielsson B 2013 Analytica Chimica Acta 766 1
[23] Xu Y, Liu M, Zhu Z and Ma J 2020 Chin. Phys. B 29 098704
[24] Nakayama T 1985 Jpn. J. Physiol. 35 375
[25] Madrid R, De La Peña E, Donovan-Rodriguez T, Belmonte C and Viana F 2009 J. Neurosci. 29 3120
[26] Liu Y, Xu W, Ma J, Alzahrani F and Hobiny A 2020 Front. Inform. Technol. Electronic Eng. 21 1387
[27] Kartelija G, Nedeljkovic M and Radenovic L 2003 Comp. Biochem. Phys. A 134 483
[28] Rodríguez-Sosa L, Calderón-Rosete G, Flores G and Porras M G 2007 Synapse 61 801
[29] Yao Z, Zhou P, Zhu Z and Ma J 2021 Neurocomputing 423 518
[30] Tzen J J, Jeng S L and Chieng W H 2003 Precis. Engin. 27 70
[31] Richter H, Misawa E A, Lucca D A and Lu H 2001 Precis. Engin. 25 128
[32] Wang Q and Quek S T 2000 Smart Mater. Struct. 9 103
[33] Elvin N G and Elvin A A 2009 J. Intel. Mat. Syst. Struct. 20 3
[34] Flynn A M and Sanders S R 2002 IEEE Tr. Power Electr. 17 8
[35] Eltamaly A M and Addoweesh K E 2016 IEEE Tr. Power Electr. 32 7663
[36] Smyth K and Kim S G 2015 IEEE Tr. Ultrason. Ferr. 62 744
[37] Zhou P, Yao Z, Ma J and Zhu Z G 2021 Chaos, Solitons & Fractals 145 110751
[38] Zhou P, Ma J and Tang J 2020 Nonlinear Dyn. 100 2353
[39] Ma J 2022 Chaos Theory Applicat. 4 1
[40] Ma J, Yang Z, Yang L and Tang J 2019 J. Zhejiang Univ. Sci. A 20 639
[41] Thanapitak S and Toumazou C 2012 IEEE Tr. Biomed. Circ. Syst. 7 296
[42] Kamermans M and Fahrenfort I 2004 Curr. Opin. Neurobiol. 14 531
[43] Gardner D and Kandel E R 1972 Science 176 675
[44] Parnas H and Parnas I 2007 Trends Neurosci. 30 54
[45] Kawato M, Sokabe M and Suzuki R 1979 Biol. Cybern. 34 81
[46] O'brien J 2014 Curr. Opin. Neurobiol. 29 64
[47] Miller A C and Pereda A E 2017 Dev. Neurobiol. 77 562
[48] Martin E A, Lasseigne A M and Miller A C 2020 Front. Neuroanat. 14 12
[49] Xu Y, Yao Z, Hobiny A and Ma J 2019 Front. Inform. Technol. Electron. Eng. 20 571
[50] Yao Z, Ma J, Yao Y and Wang C 2019 Nonlinear Dyn. 96 205
[51] Liu Z, Wang C, Zhang G and Zhang Y 2019 Int. J. Mod. Phys. B 33 1950170
[52] Liu Y, Huang W, Wang X, Liang R, Wang J, Yu B, Ren T L and Xu J 2018 IEEE J. Electron. Devi. 7 13
[53] Aghnout S and Karimi G 2019 Integr. 64 184
[54] Wang W, He C, Wang Z, Cheng J, Mo X S, Tian K, Fan D G, Luo X, Yuan M M and Kurth J 2021 Neurocomputing 456 23
[55] Ma S Y, Zhou P, Ma J and Wang C 2020 Int. J. Mod. Phys. B 34 2050074
[56] Liu Z L, Zhou P, Ma J, Hobiny A and Alzahrani F 2020 Chaos, Solitons & Fractals 131 109533
[57] Rajagopal K, Jafari S, Moroz I, Karthikeyan A and Srinivasan A 2021 Chaos 31 073117
[58] Rajagopal K, Ramesh A, Moroz I, Duraisamy P and Karthikeyan A 2021 Chaos 31 063111
[59] Rajagopal K, Ramesh A, Moroz I, Duraisamy P and Karthikeyan A 2021 Chaos 31 063111
[60] Rajagopal K, Jafari S, Li C, Karthikeyane A and Duraisamya P 2021 Chaos, Solitons & Fractals 146 110855
[61] Rajagopal K, Jafari A, He S, Parastesh F, Jafari S and Hussain I 2021 Eur. Phys. J. Spec. Top.
[62] Zhou C, Wang C, Sun Y and Wang Y 2020 Neurocomput. 403 211
[63] Yao W, Wang C, Cao J, Sun Y and Zhou C 2019 Neurocomput. 363 281
[64] Yao W, Wang C, Sun Y, Zhou C and Lin H 2020 Neurocomputing 404 367
[65] Zhang Y, Zhou P, Yao Z and Ma J 2021 Pramana J. Phys. 95 49
[1] Diffusive field coupling-induced synchronization between neural circuits under energy balance
Ya Wang(王亚), Guoping Sun(孙国平), and Guodong Ren(任国栋). Chin. Phys. B, 2023, 32(4): 040504.
[2] Hopf bifurcation and phase synchronization in memristor-coupled Hindmarsh-Rose and FitzHugh-Nagumo neurons with two time delays
Zhan-Hong Guo(郭展宏), Zhi-Jun Li(李志军), Meng-Jiao Wang(王梦蛟), and Ming-Lin Ma(马铭磷). Chin. Phys. B, 2023, 32(3): 038701.
[3] Current bifurcation, reversals and multiple mobility transitions of dipole in alternating electric fields
Wei Du(杜威), Kao Jia(贾考), Zhi-Long Shi(施志龙), and Lin-Ru Nie(聂林如). Chin. Phys. B, 2023, 32(2): 020505.
[4] Influence of coupling asymmetry on signal amplification in a three-node motif
Xiaoming Liang(梁晓明), Chao Fang(方超), Xiyun Zhang(张希昀), and Huaping Lü(吕华平). Chin. Phys. B, 2023, 32(1): 010504.
[5] Power-law statistics of synchronous transition in inhibitory neuronal networks
Lei Tao(陶蕾) and Sheng-Jun Wang(王圣军). Chin. Phys. B, 2022, 31(8): 080505.
[6] Effect of astrocyte on synchronization of thermosensitive neuron-astrocyte minimum system
Yi-Xuan Shan(单仪萱), Hui-Lan Yang(杨惠兰), Hong-Bin Wang(王宏斌), Shuai Zhang(张帅), Ying Li(李颖), and Gui-Zhi Xu(徐桂芝). Chin. Phys. B, 2022, 31(8): 080507.
[7] Multi-target ranging using an optical reservoir computing approach in the laterally coupled semiconductor lasers with self-feedback
Dong-Zhou Zhong(钟东洲), Zhe Xu(徐喆), Ya-Lan Hu(胡亚兰), Ke-Ke Zhao(赵可可), Jin-Bo Zhang(张金波),Peng Hou(侯鹏), Wan-An Deng(邓万安), and Jiang-Tao Xi(习江涛). Chin. Phys. B, 2022, 31(7): 074205.
[8] Synchronization of nanowire-based spin Hall nano-oscillators
Biao Jiang(姜彪), Wen-Jun Zhang(张文君), Mehran Khan Alam, Shu-Yun Yu(于淑云), Guang-Bing Han(韩广兵), Guo-Lei Liu(刘国磊), Shi-Shen Yan(颜世申), and Shi-Shou Kang(康仕寿). Chin. Phys. B, 2022, 31(7): 077503.
[9] Bifurcation analysis of visual angle model with anticipated time and stabilizing driving behavior
Xueyi Guan(管学义), Rongjun Cheng(程荣军), and Hongxia Ge(葛红霞). Chin. Phys. B, 2022, 31(7): 070507.
[10] The transition from conservative to dissipative flows in class-B laser model with fold-Hopf bifurcation and coexisting attractors
Yue Li(李月), Zengqiang Chen(陈增强), Mingfeng Yuan(袁明峰), and Shijian Cang(仓诗建). Chin. Phys. B, 2022, 31(6): 060503.
[11] A novel car-following model by sharing cooperative information transmission delayed effect under V2X environment and its additional energy consumption
Guang-Han Peng(彭光含), Te-Ti Jia(贾特提), Hua Kuang(邝华), Hui-Li Tan(谭惠丽), and Tao Chen(陈陶). Chin. Phys. B, 2022, 31(5): 058901.
[12] Synchronization in multilayer networks through different coupling mechanisms
Xiang Ling(凌翔), Bo Hua(华博), Ning Guo(郭宁), Kong-Jin Zhu(朱孔金), Jia-Jia Chen(陈佳佳), Chao-Yun Wu(吴超云), and Qing-Yi Hao(郝庆一). Chin. Phys. B, 2022, 31(4): 048901.
[13] Collective behavior of cortico-thalamic circuits: Logic gates as the thalamus and a dynamical neuronal network as the cortex
Alireza Bahramian, Sajjad Shaukat Jamal, Fatemeh Parastesh, Kartikeyan Rajagopal, and Sajad Jafari. Chin. Phys. B, 2022, 31(2): 028901.
[14] Explosive synchronization: From synthetic to real-world networks
Atiyeh Bayani, Sajad Jafari, and Hamed Azarnoush. Chin. Phys. B, 2022, 31(2): 020504.
[15] Measure synchronization in hybrid quantum-classical systems
Haibo Qiu(邱海波), Yuanjie Dong(董远杰), Huangli Zhang(张黄莉), and Jing Tian(田静). Chin. Phys. B, 2022, 31(12): 120503.
No Suggested Reading articles found!