Please wait a minute...
Chin. Phys. B, 2023, Vol. 32(3): 030201    DOI: 10.1088/1674-1056/aca602
GENERAL Prev   Next  

Inverse stochastic resonance in modular neural network with synaptic plasticity

Yong-Tao Yu(于永涛) and Xiao-Li Yang(杨晓丽)
School of Mathematics and Statistics, Shaanxi Normal University, Xi'an 710062, China
Abstract  This work explores the inverse stochastic resonance (ISR) induced by bounded noise and the multiple inverse stochastic resonance induced by time delay by constructing a modular neural network, where the modified Oja's synaptic learning rule is employed to characterize synaptic plasticity in this network. Meanwhile, the effects of synaptic plasticity on the ISR dynamics are investigated. Through numerical simulations, it is found that the mean firing rate curve under the influence of bounded noise has an inverted bell-like shape, which implies the appearance of ISR. Moreover, synaptic plasticity with smaller learning rate strengthens this ISR phenomenon, while synaptic plasticity with larger learning rate weakens or even destroys it. On the other hand, the mean firing rate curve under the influence of time delay is found to exhibit a decaying oscillatory process, which represents the emergence of multiple ISR. However, the multiple ISR phenomenon gradually weakens until it disappears with increasing noise amplitude. On the same time, synaptic plasticity with smaller learning rate also weakens this multiple ISR phenomenon, while synaptic plasticity with larger learning rate strengthens it. Furthermore, we find that changes of synaptic learning rate can induce the emergence of ISR phenomenon. We hope these obtained results would provide new insights into the study of ISR in neuroscience.
Keywords:  inverse stochastic resonance      synaptic plasticity      modular neural network  
Received:  03 September 2022      Revised:  12 November 2022      Accepted manuscript online:  25 November 2022
PACS:  02.50.-r (Probability theory, stochastic processes, and statistics)  
  05.40.-a (Fluctuation phenomena, random processes, noise, and Brownian motion)  
  87.85.dq (Neural networks)  
  02.30.Ks (Delay and functional equations)  
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 11972217).
Corresponding Authors:  Xiao-Li Yang     E-mail:

Cite this article: 

Yong-Tao Yu(于永涛) and Xiao-Li Yang(杨晓丽) Inverse stochastic resonance in modular neural network with synaptic plasticity 2023 Chin. Phys. B 32 030201

[1] Zhang H, Wang Q, He X and Chen G 2014 Neural Netw. 49 107
[2] Boccaletti S, Kurths J, Osipov G, Valladares D L and Zhou C S 2002 Phys. Rep. 366 101
[3] Pikovsky A, Rosenblum M and Kurths J 2001 Am. J. Phys. 70 655
[4] Liu S, Sun Z, Zhao N and Xu W 2022 Int. J. Bifurcat. Chaos 32 2250018
[5] Yang X, Li N and Sun Z 2019 Nonlinear Dyn. 98 1029
[6] Gammaitoni L, Hänggi P, Jung P and Marchesoni F 2009 Eur. Phys. J. B 69 3
[7] Ozer M, Perc M and Uzuntarla M 2009 Phys. Lett. A 373 964
[8] Wang Q, Zhang H, Perc M and Chen G 2012 Commun. Nonlinear Sci. 17 3979
[9] Masoliver J, Robinson A and Weiss G H 1995 Phys. Rev. E 51 4021
[10] Zheng Y, Wang Q and Danca M F 2014 Cogn. Neurodyn. 8 143
[11] Liu Y, Sun Z and Yang X 2021 Appl. Math. Comput. 409 126384
[12] Paydarfar D, Forger D B and Clay J R 2006 J. Neurophysiol. 96 3338
[13] Uzuntarla M, Cressman J R, Ozer M and Ernest B 2012 BMC Neurosci. 13 181
[14] Uzuntarla M, Barreto E and Torres J J 2017 PLoS Comput. Biol. 13 1005646
[15] Uzuntarla M 2013 Phys. Lett. A 377 2585
[16] Buchin A J, Roth A, Hausser M and Rieubland S 2016 PLoS Comput. Biol. 12 1005000
[17] Hilgetag C C, Burns G A P C, O'Neill M A, Scannell J W and Young M P 2000 Philos. Trans. R. Soc. Lond., B, Biol. Sci. 355 91
[18] Zamora-López G, Zhou C and Kurths J 2009 Chaos 4 1
[19] Tuckwell H C and Jost J 2012 Physica A 391 5311
[20] Guo D 2011 Cogn. Neurodyn. 5 293
[21] Li D, Cui X and Yang Y 2018 Neurocomputing 287 52
[22] Lu L, Jia Y, Ge M, Xu Y and Li A 2020 Nonlinear Dyn. 100 877
[23] Feng Z H, Lan X J and Zhu X D 2007 Int. J. Nonlinear Mech. 42 1170
[24] Cai G Q and Wu C 2004 Probabilistic Eng. Mech. 19 197
[25] Kai-Leung Y and Lei Y M 2010 Chin. Phys. B 19 010503
[26] Wang J and Li D X 2017 Journal of North University of China (Natural Science Edition) 38 31
[27] Kandel E R, Schwartz J H and Jessell T M 2014 Principles of Neural Science, 5th edn. (New York: McGraw-Hill)
[28] Zhang N, Li D and Xing Y 2021 Eur. Phys. J. B 94 1
[29] Holtmaat A and Svoboda K 2009 Nat. Rev. Neurosci. 10 647
[30] Schmidt-Hieber C, Jonas P and Bischofberger J 2004 Nature 10 244
[31] Munakata Y and Pfaffly J 2010 Dev Sci. 7 141
[32] Martin S J, Grimwood P D and Morris R 2000 Annu. Rev. Neurosci. 23 649
[33] Hebb D O 2022 The Organization of Behavior: A Neuropsychological Theory (New York: Psychology Press)
[34] Oja E 1982 J. Math. Biol. 15 267
[35] Gerstner W, Kempter R, Hemmen J and Wagner H 1996 Nature 383 76
[36] Yao Z L, Yang X L and Sun Z K 2018 Chaos 28 083120
[37] Yang X L, Wang J Y and Sun Z K 2017 Nonlinear Dyn. 89 2593
[38] Newman M and Watts D J 1999 Phys. Lett. A 263 341
[39] Hodgkin A L and Huxley A F 1990 Bull. Math. Biol. 52 25
[40] Pankratova E V, Polovinkin A V and Mosekilde E 2005 Eur. Phys. J. B 45 391
[1] Inhibitory effect induced by fractional Gaussian noise in neuronal system
Zhi-Kun Li(李智坤) and Dong-Xi Li(李东喜). Chin. Phys. B, 2023, 32(1): 010203.
[2] Artificial synaptic behavior of the SBT-memristor
Gang Dou(窦刚), Ming-Long Dou(窦明龙), Ren-Yuan Liu(刘任远), and Mei Guo(郭梅). Chin. Phys. B, 2021, 30(7): 078401.
[3] Synaptic plasticity and classical conditioning mimicked in single indium-tungsten-oxide based neuromorphic transistor
Rui Liu(刘锐), Yongli He(何勇礼), Shanshan Jiang(姜珊珊), Li Zhu(朱力), Chunsheng Chen(陈春生), Ying Zhu(祝影), and Qing Wan(万青). Chin. Phys. B, 2021, 30(5): 058102.
[4] Implementation of synaptic learning rules by TaOx memristors embedded with silver nanoparticles
Yue Ning(宁玥), Yunfeng Lai(赖云锋), Jiandong Wan(万建栋), Shuying Cheng(程树英), Qiao Zheng(郑巧), and Jinling Yu(俞金玲). Chin. Phys. B, 2021, 30(4): 047301.
[5] High-performance synaptic transistors for neuromorphic computing
Hai Zhong(钟海), Qin-Chao Sun(孙勤超), Guo Li(李果), Jian-Yu Du(杜剑宇), He-Yi Huang(黄河意), Er-Jia Guo(郭尔佳), Meng He(何萌), Can Wang(王灿), Guo-Zhen Yang(杨国桢), Chen Ge(葛琛), Kui-Juan Jin(金奎娟). Chin. Phys. B, 2020, 29(4): 040703.
[6] Electronic synapses based on ultrathin quasi-two-dimensional gallium oxide memristor
Shuopei Wang(王硕培), Congli He(何聪丽), Jian Tang(汤建), Rong Yang(杨蓉), Dongxia Shi(时东霞), Guangyu Zhang(张广宇). Chin. Phys. B, 2019, 28(1): 017304.
No Suggested Reading articles found!