Effects of Time Delay on Bifurcation and Synchronization of Flux-Coupled and Chemically Coupled Neurons
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摘要:
以化学突触耦合神经元模型为基础,讨论了抑制性及兴奋性条件下达到同步的区别及同步的类型。并根据磁通耦合对神经元放电的影响,讨论了具有时滞、磁通耦合和化学耦合Morris-Lecar (ML)神经元模型的放电状态、分岔类型及其同步情况。发现具有磁通耦合和化学耦合ML神经元系统在不同参数下会产生丰富的逆倍周期分岔或加周期分岔行为。而时滞的引入,虽然可以增加系统的周期性,但同时也会破环系统同步。相反,适当的耦合强度能够增加同步。
Abstract:Based on the chemical synaptic coupled neuron model, the differences and types of synchronization under inhibitory and excitatory conditions were discussed. According to the effect of the magnetic flow coupling on neuron discharge, the discharge states, bifurcation types and synchronization of the Morris-Lecar (ML) neuron models with time delay, magnetic flux coupling and chemical coupling, were analyzed. The results show that, the ML neuronal systems with magnetic flow coupling and chemical coupling can produce rich inverse periodic bifurcation or incremental periodic bifurcation behaviors under different parameters. The introduction of time delay, although can increase the periodicity of the system, will also break the system synchronization. Conversely, an appropriate coupling strength can enhance synchronization.
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Key words:
- time delay /
- flux-coupled /
- bifurcation /
- synchronization
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图 2 系统(1)在抑制性条件下的时间历程图与相图:(a)
$D = 1.98$ 时的时间历程图;(b)$D = 1.98$ 时的相图;(c)$D = 2.04$ 时的时间历程图;(d)$D = 2.04$ 时的相图Figure 2. Time history diagrams and phase diagrams of system (1) under inhibition: (a) the time history diagram at time
$D = 1.98$ ; (b) the phase diagram at time$D = 1.98$ ; (c) the time history diagram at time$D = 2.04$ ; (d) the phase diagram at time$D = 2.04$ 图 3 系统(1)在兴奋性条件下的时间历程图与相图:(a)
$D = 0.081$ 时的时间历程图;(b)$D = 0.081$ 时的相图;(c)$D = 0.175$ 时的时间历程图;(d)$D = 0.175$ 时的相图Figure 3. Time history diagrams and phase diagrams of system (1) under excitatory condition: (a) the time history diagram at time
$D = 0.081$ ; (b) the phase diagram at time$D = 0.081$ ; (c) the time history diagram at time$D = 0.175$ ; (d) the phase diagram at time$D = 0.175$ 图 5 系统(2)在不同反馈增益下的时间历程图与相图:(a)
$ {k_1} = 0.2 $ 的时间历程图;(b)$ {k_1} = - 0.5 $ 的时间历程图;(c)$ {k_1} = - 1.08 $ 的时间历程图;(d)$ {k_1} = 0.2 $ 的相图;(e)$ {k_1} = - 0.5 $ 的相图;(f)$ {k_1} = - 1.08 $ 的相图Figure 5. Time history diagrams and phase diagrams of system (2) under different feedback gains: (a) the time history diagram at time
$ {k_1} = 0.2 $ ; (b) the time history diagram at time$ {k_1} = - 0.5 $ ; (c) the time history diagram at time$ {k_1} = - 1.08 $ ; (d) the phase diagram at time$ {k_1} = 0.2 $ ; (e) the phase diagram at time$ {k_1} = - 0.5 $ ; (f) the phase diagram at time$ {k_1} = - 1.08 $ 图 8 反馈增益
$ {k_1} $ ,$ {k_2} $ 与不同参数的双参数分岔图:(a)$ {k_1} $ 与$V_{\rm{K}}$ 双参数分岔图;(b)$ {k_1} $ 与$ V_{\rm Ca} $ 双参数分岔图;(c)$ {k_2} $ 与$ g_{\rm Ca} $ 双参数分岔图;(d)$ {k_2} $ 与$ g_{\rm{K}} $ 双参数分岔图Figure 8. Two-parameter bifurcation diagrams with feedback gains and different parameters: (a) the
$ {k_1} $ and$ V_{\rm{K}} $ two-parameter bifurcation diagram; (b) the$ {k_1} $ and$ V_{\rm Ca} $ two-parameter bifurcation diagram; (c) the$ {k_2} $ and$ g_{\rm Ca} $ two-parameter bifurcation diagram; (d) the$ {k_2} $ and$ g_{\rm{K}} $ two-parameter bifurcation diagram图 11 系统(2)在无时滞状态下的相似函数图:(a) 反馈增益
${k_1}$ 与参数$\alpha $ 相似函数图;(b) 反馈增益${k_1}$ 与参数${V_{{\rm{syn}}}}$ 相似函数图;(c) 反馈增益${k_1}$ 与耦合强度D相似函数图Figure 11. Similar function diagrams of system (2) without delay: (a) the feedback gain
${k_1}$ and parameter$\alpha $ similarity function diagram; (b) the feedback gain${k_1}$ and parameter${V_{{\rm{syn}}}}$ similarity function diagram; (c) the feedback gain${k_1}$ and coupling strength similarity function diagram -
[1] 寿天德. 神经生物学[M]. 北京: 高等教育出版社, 2006.SHOU Tiande. Neurobiology[M]. Beijing: Higher Education Press, 2006. (in Chinese) [2] 张艳娇, 李美生, 陆启韶. ML神经元的放电模式及时滞对神经元同步的影响[J]. 动力学与控制学报, 2009, 7(1): 19-23 doi: 10.3969/j.issn.1672-6553.2009.01.005ZHANG Yanjiao, LI Meisheng, LU Qishao. Effect of discharge mode delay of ML neurons on neuronal synchronization[J]. Journal of Dynamics and Control, 2009, 7(1): 19-23.(in Chinese) doi: 10.3969/j.issn.1672-6553.2009.01.005 [3] MA J, ZHANG G, HAYAT T, et al. Mo del electrical activity of neuron under electric field[J]. Nonlinear Dynamics, 2019, 95: 1585-1598. doi: 10.1007/s11071-018-4646-7 [4] MA J, WANG Y, WANG C, et al. Mode selection in electrical activities of myocardial cell exposed to electromagnetic radiation[J]. Chaos, Solitons and Fractals, 2017, 99: 219-255. doi: 10.1016/j.chaos.2017.04.016 [5] LV M, MA J. Multiple modes of electrical activities in a new neuron model under electromagnetic radiation[J]. Neurocomputing, 2016, 205: 375-381. doi: 10.1016/j.neucom.2016.05.004 [6] 曲良辉, 都琳, 胡海威, 等. 电磁刺激对FHN神经元系统的调控作用[J]. 动力学与控制学报, 2020, 18(1): 40-48 doi: 10.6052/1672-6553-2020-003QU Lianghui, DU Lin, HU Haiwei, et al. Regulation of FHN neuronal system by electromagnetic stimulation[J]. Journal of Dynamics and Control, 2020, 18(1): 40-48.(in Chinese) doi: 10.6052/1672-6553-2020-003 [7] SOUDEH M, FAHIMEH N, SAJAD J, et al. Chemical and electrical synapse-modulated dynamical properties of coupled neurons under magnetic flow[J]. Applied Mathematics and Computation, 2019, 348: 42-56. doi: 10.1016/j.amc.2018.11.030 [8] GUO S, XU Y, WANG C, et al. Collective response, synapse coupling and field coupling in neuronal network[J]. Chaos, Solitons and Fractals, 2017, 105: 120-127. doi: 10.1016/j.chaos.2017.10.019 [9] 王青云, 石霞, 陆启韶. 神经元耦合系统的同步动力学[M]. 北京: 科学出版社, 2008.WANG Qingyun, SHI Xia, LU Qishao. Synchronous Dynamics of Neuron Coupling Systems[M]. Beijing: Science Press, 2008. (in Chinese) [10] WANG Q, LU Q, ZHENG Y. Conduction delay-aided synchronization in two coupled Chay neurons with inhibitory synapse[J]. Journal of Biophysics, 2005, 21(6): 449-456. [11] WANG Q, LU Q, CHEN G, et al. Bifurcation and synchronization of synaptically coupled FHN models with time delay[J]. Chaos, Solitons and Fractals, 2007, 39(2): 918-925. [12] WANG Q, LU Q. Time-delay enhanced synchronization and regularization in two coupled chaotic neurons[J]. Chinese Physics Letters, 2005, 22(6): 543-546. [13] 谢一丁, 王征平, 刘帅. 复杂网络上耦合神经系统的非聚类相同步[J]. 应用数学和力学, 2020, 41(6): 627-635XIE Yiding, WANG Zhengping, LIU Shuai. Unclustered phase synchronization of coupled nervous systems on complex networks[J]. Applied Mathematics and Mechanics, 2020, 41(6): 627-635.(in Chinese) [14] 邬开俊, 单亚洲, 王春丽, 等. 时滞作用下的化学突触耦合的Hindmarsh-Rose神经元同步研究[J]. 力学季刊, 2017, 38(1): 123-134 doi: 10.15959/j.cnki.0254-0053.2017.01.014WU Kaijun, SHAN Yazhou, WANG Chunli, et al. Synchronization of chemical synaptic coupling with time delay in Hindmarsh-Rose neurons[J]. Chinese Quarterly of Mechanics, 2017, 38(1): 123-134.(in Chinese) doi: 10.15959/j.cnki.0254-0053.2017.01.014 [15] DHAMALA M, JIRSA V, DING M. Enhancement of neural synchrony by time delay[J]. Physical Review Letters, 2004, 92(7): 074104. doi: 10.1103/PhysRevLett.92.074104 [16] 李小虎, 张定一, 宋自根. 时滞耦合惯性项神经系统的多混沌路径共存[J]. 应用数学和力学, 2020, 41(6): 636-645LI Xiaohu, ZHANG Dingyi, SONG Zigen. Coexistence of multiple chaotic paths in neural systems with time-delay coupled inertial terms[J]. Applied Mathematics and Mechanics, 2020, 41(6): 636-645.(in Chinese)