Collective dynamics of neural network with distance dependent field coupling

被引:11
|
作者
Remi, T. [1 ]
Subha, P. A. [1 ]
Usha, K. [2 ]
机构
[1] Univ Calicut, Farook Coll, Dept Phys, Calicut 673632, Kerala, India
[2] Govt Arts & Sci Coll, Dept Phys, Nadapuram 673506, Kerala, India
关键词
Neuron network; Field coupling; Distance dependent coupling; Synchronicity; Chimera states; ELECTRICAL-ACTIVITIES; MODEL;
D O I
10.1016/j.cnsns.2022.106390
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this work, we analyse the collective dynamics of Hindmarsh-Rose neurons with distance dependent memristor and mean field coupling, realised by power law exponent. The memristor characterises the electromagnetic field induced by the magnetic flux. The mean field represents the global effect in the neurons which are usually influenced by enormous number of neighbours. The uniform global mean field drives the memristive neural network from Amplitude Death to synchronised bursting state. Desynchronised, Mixed Oscillatory, imperfect and complete synchronised states are attained by the network due to the interplay between memristor and uniform mean field coupling. The synchronisation scenario has been analysed in distance dependent memristive and mean field network, by varying the values of power law exponent. The distance dependent mean field coupling induces chimera and multichimera states in the system. The strength of incoherence and discontinuity measure are calculated to distinguish incoherent, chimera, multichimera and coherent states. Coherent states are unattainable for high values of power law exponent. The existence of chimera states in system with long range interactions are verified by parameter space. This work may give better understanding about the different aspects of synchronisation involved in neural networks with distance dependent coupling and proves that the non local synaptic coupling is not a prerequisite to realise chimera states.(c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] The Temperature Dependent Collective Dynamics of Liquid Sodium
    Patel, A. B.
    Khambholja, S. G.
    Bhatt, N. K.
    Thakore, B. Y.
    Vyas, P. R.
    Jani, A. R.
    SOLID STATE PHYSICS, PTS 1 AND 2, 2012, 1447 : 571 - +
  • [42] Control the collective behaviors in a functional neural network
    Yao, Zhao
    Wang, Chunni
    CHAOS SOLITONS & FRACTALS, 2021, 152
  • [43] On the Coupling between the Collective Dynamics of Proteins and Their Hydration Water
    Nibali, Valeria Conti
    D'Angelo, Giovanna
    Paciaroni, Alessandro
    Tobias, Douglas J.
    Tarek, Mounir
    JOURNAL OF PHYSICAL CHEMISTRY LETTERS, 2014, 5 (07): : 1181 - 1186
  • [44] Stochastic dynamics of a neural field lattice model with state dependent nonlinear noise
    Wang, Xiaoli
    Kloeden, Peter E.
    Han, Xiaoying
    NODEA-NONLINEAR DIFFERENTIAL EQUATIONS AND APPLICATIONS, 2021, 28 (04):
  • [45] Evaluation on stability of stope structure based on nonlinear dynamics of coupling artificial neural network
    Cai, Meifeng
    Lai, Xingping
    Journal of University of Science and Technology Beijing: Mineral Metallurgy Materials (Eng Ed), 2002, 9 (01): : 1 - 4
  • [46] Evaluation on stability of stope structure based on nonlinear dynamics of coupling artificial neural network
    Cai, MF
    Lai, XP
    JOURNAL OF UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING, 2002, 9 (01): : 1 - 4
  • [47] Stochastic dynamics of a neural field lattice model with state dependent nonlinear noise
    Xiaoli Wang
    Peter E. Kloeden
    Xiaoying Han
    Nonlinear Differential Equations and Applications NoDEA, 2021, 28
  • [48] Estimation of the Lateral Distance between Vehicle and Lanes Using Convolutional Neural Network and Vehicle Dynamics
    Zhang, Xiang
    Yang, Wei
    Tang, Xiaolin
    He, Zhonghua
    APPLIED SCIENCES-BASEL, 2018, 8 (12):
  • [49] A review for dynamics of collective behaviors of network of neurons
    MA Jun
    TANG Jun
    Science China(Technological Sciences), 2015, 58 (12) : 2038 - 2045
  • [50] A review for dynamics of collective behaviors of network of neurons
    Ma Jun
    Tang Jun
    SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2015, 58 (12) : 2038 - 2045