不同耦合下混沌神经元网络的同步

吴望生; 唐国宁

doi:10.7498/aps.61.070505

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摘要

采用Hindmarsh-Rose神经元动力学模型, 对二维点阵上的神经元网络的同步进行了研究. 为了解不同耦合对网络同步的影响, 提出了一般反馈耦合、分层反馈耦合和分层局域平均场反馈耦合三种方案.研究表明:在耦合强度较小的近邻耦合下, 一般反馈耦合不能使网络达到完全同步, 而分层反馈耦合和分层局域平均场反馈耦合可以使网络出现局部同步和全局同步. 不同形式的耦合会导致网络出现不同的斑图, 随着耦合强度的增大, 网络从不同步到同步的过程也不相同, 一般反馈耦合和分层反馈耦合网络是突然出现全局同步, 同步之前网络出现非周期性的相干斑图; 对于分层局域平均场反馈耦合网络, 同层神经元之间先出现从簇放电同步到同步的转变, 形成靶波, 然后同步区由中心向外逐渐扩大, 最终达到网络的全局同步. 这些结果表明, 只有适当的耦合才能实现信号的无损耗的传递. 此外我们发现分层局域平均场反馈耦合可以促进网络的同步.

关键词:

作者及机构信息

1.
广西师范大学物理科学与技术学院, 桂林 541004

基金项目: 国家自然科学基金(批准号:11165004, 10765002)资助的课题.

参考文献

[1]	Pecora L M, Carroll T L 1990 Phys. Rev. Lett. 64 821
[2]	Baptista M S, Moukam Kakmeni F M, Grebogi C 2010 Phys. Rev.E 82 036203
[3]	Rappel W J, Karma A 1996 Phys. Rev. Lett. 77 3256
[4]	Dhamala M, Jirsa V K, DingMZ 2004 Phys. Rev. Lett. 92 074104
[5]	Rosenblum M G, Pikovsky A S 2004 Phys. Rev. Lett. 92 114102
[6]	Yu H J, Tong W J 2009 Acta Phys. Sin. 58 2977 (in Chinese)[于洪吉, 童伟君 2009 物理学报 58 2977]
[7]	Zhou J, Liu Z H 2008 Phys. Rev. E 77 056213
[8]	Tang Y, Qiu R, Fang J A, Miao Q Y 2008 Phys. Lett. A 372 4425
[9]	Wang H X, Lu Q S, Wang Q Y 2005 Chin. Phys. Lett. 22 2173
[10]	He G G, Zhu P, Chen H P, Xie X P 2010 Acta Phys. Sin. 595307(in Chinese)[何国光, 朱萍, 陈宏平, 谢小平 2010物理学报 59 5307]
[11]	Shahverdiev E M, Shore K A 2005 Phys. Rev. E 71 016201
[12]	Wang H X, Lu Q S, Shi X 2010 Chin. Phys. B 19 060509
[13]	Zeitler M, Daffertshofer A, Gielen C C A M 2009 Phys. Rev. E79 065203
[14]	Englert A, Kinzel W, Aviad Y, Butkovski M, Reidler I, Zigzag M,Kanter I, Rosenbluh M 2010 Phys. Rev. Lett. 104 114102
[15]	Ma J, Su WT, Gao J Z 2010 Acta Phys. Sin. 59 1554 (in Chinese)[马军, 苏文涛, 高加振 2010 物理学报 59 1554]
[16]	Yu J, Hu C, Jiang H J, Teng Z D 2011 Neurocomputing 74 1776
[17]	Wang Q Y, Chen G R, Perc M 2011 PLoS ONE 6 e15851
[18]	Shi X, Lu Q S 2007 Chin. Phys. Lett. 24 636
[19]	Sheeba J H, Chandrasekar V K, Lakshmanan M 2009 Phys. Rev.E 79 055203
[20]	Huerta R, Bhazenov M, Rabinovich M I 1998 Europhys. Lett. 43719
[21]	Mainieri M S, Erichsen Jr R, Brunnet L G 2005 Physica A 354663
[22]	Hindmarsh J L, Rose R M 1984 Proc. R. Soc. Lond. B 221 87
[23]	Lu Q S, Liu S Q, Liu F, Wang Q Y, Hou Z H, Zheng Y H 2008Adv. Mech. 38 766 (in Chinese)[陆启韶, 刘深泉, 刘峰, 王青云, 候中怀, 郑艳红 2008 力学进展 38 766]

施引文献

[1]	Pecora L M, Carroll T L 1990 Phys. Rev. Lett. 64 821
[2]	Baptista M S, Moukam Kakmeni F M, Grebogi C 2010 Phys. Rev.E 82 036203
[3]	Rappel W J, Karma A 1996 Phys. Rev. Lett. 77 3256
[4]	Dhamala M, Jirsa V K, DingMZ 2004 Phys. Rev. Lett. 92 074104
[5]	Rosenblum M G, Pikovsky A S 2004 Phys. Rev. Lett. 92 114102
[6]	Yu H J, Tong W J 2009 Acta Phys. Sin. 58 2977 (in Chinese)[于洪吉, 童伟君 2009 物理学报 58 2977]
[7]	Zhou J, Liu Z H 2008 Phys. Rev. E 77 056213
[8]	Tang Y, Qiu R, Fang J A, Miao Q Y 2008 Phys. Lett. A 372 4425
[9]	Wang H X, Lu Q S, Wang Q Y 2005 Chin. Phys. Lett. 22 2173
[10]	He G G, Zhu P, Chen H P, Xie X P 2010 Acta Phys. Sin. 595307(in Chinese)[何国光, 朱萍, 陈宏平, 谢小平 2010物理学报 59 5307]
[11]	Shahverdiev E M, Shore K A 2005 Phys. Rev. E 71 016201
[12]	Wang H X, Lu Q S, Shi X 2010 Chin. Phys. B 19 060509
[13]	Zeitler M, Daffertshofer A, Gielen C C A M 2009 Phys. Rev. E79 065203
[14]	Englert A, Kinzel W, Aviad Y, Butkovski M, Reidler I, Zigzag M,Kanter I, Rosenbluh M 2010 Phys. Rev. Lett. 104 114102
[15]	Ma J, Su WT, Gao J Z 2010 Acta Phys. Sin. 59 1554 (in Chinese)[马军, 苏文涛, 高加振 2010 物理学报 59 1554]
[16]	Yu J, Hu C, Jiang H J, Teng Z D 2011 Neurocomputing 74 1776
[17]	Wang Q Y, Chen G R, Perc M 2011 PLoS ONE 6 e15851
[18]	Shi X, Lu Q S 2007 Chin. Phys. Lett. 24 636
[19]	Sheeba J H, Chandrasekar V K, Lakshmanan M 2009 Phys. Rev.E 79 055203
[20]	Huerta R, Bhazenov M, Rabinovich M I 1998 Europhys. Lett. 43719
[21]	Mainieri M S, Erichsen Jr R, Brunnet L G 2005 Physica A 354663
[22]	Hindmarsh J L, Rose R M 1984 Proc. R. Soc. Lond. B 221 87
[23]	Lu Q S, Liu S Q, Liu F, Wang Q Y, Hou Z H, Zheng Y H 2008Adv. Mech. 38 766 (in Chinese)[陆启韶, 刘深泉, 刘峰, 王青云, 候中怀, 郑艳红 2008 力学进展 38 766]

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不同耦合下混沌神经元网络的同步

1. 广西师范大学物理科学与技术学院, 桂林 541004

基金项目:

国家自然科学基金(批准号:11165004, 10765002)资助的课题.

收稿日期: 2011-07-08

修回日期: 2012-04-05

刊出日期: 2012-04-05

摘要: 采用Hindmarsh-Rose神经元动力学模型, 对二维点阵上的神经元网络的同步进行了研究. 为了解不同耦合对网络同步的影响, 提出了一般反馈耦合、分层反馈耦合和分层局域平均场反馈耦合三种方案.研究表明:在耦合强度较小的近邻耦合下, 一般反馈耦合不能使网络达到完全同步, 而分层反馈耦合和分层局域平均场反馈耦合可以使网络出现局部同步和全局同步. 不同形式的耦合会导致网络出现不同的斑图, 随着耦合强度的增大, 网络从不同步到同步的过程也不相同, 一般反馈耦合和分层反馈耦合网络是突然出现全局同步, 同步之前网络出现非周期性的相干斑图; 对于分层局域平均场反馈耦合网络, 同层神经元之间先出现从簇放电同步到同步的转变, 形成靶波, 然后同步区由中心向外逐渐扩大, 最终达到网络的全局同步. 这些结果表明, 只有适当的耦合才能实现信号的无损耗的传递. 此外我们发现分层局域平均场反馈耦合可以促进网络的同步.

关键词:

Synchronizations of chaotic neuronal networks under different couplings

1.
College of Physics and Technology, Guangxi Normal University, Guilin 541004, China

Fund Project:

Project supported by the National Natural Science Foundation of China (Grant Nos. 11165004, 10765002).

Received Date: 08 July 2011

Accepted Date: 05 April 2012

Published Online: 05 April 2012

Abstract: The synchronization of a two-dimensional (2D) neuronal network is investigated, based on the dynamical model of Hindmarsh-Rose neuron. In order to know the effects of different types of coupling on the synchronization of a network, we propose three coupling schemes. They are the general feedback coupling, the hierarchical feedback couplings with and without local mean field. The numerical results show that when the neighbor coupling strength is small, the hierarchical feedback couplings with and without local mean field can achieve local and global synchronizations of the network, whereas the general feedback coupling cannot achieve global synchronization. Different couplings generate different patterns in the corresponding network, so that the processes of the transition from asynchronization to synchronization in the networks are different. With the increase of coupling strength, the synchronization in the network with the general feedback or hierarchical feedback couplings is suddenly established, and the networks exhibit different coherent patterns that are aperiodic before the global synchronization occurs. However, the network with hierarchical feedback couplings and local mean field exhibits the different synchronous processes. The neurons in the same layer first achieve the transition from bursting synchronization to global synchronization, leading to the formation of target wave. Then, the synchronization region gradually expands from the center of the network. Finally, the whole networks can achieve synchronization. These results show that the lossless signal transmission can be achieved only if the appropriate coupling is applied. In addition, we find that the hierarchical feedback coupling with local mean field can facilitate synchronization.

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