Phase-response synchronization in neuronal population

被引:11
|
作者
Jiao XianFa [1 ]
Zhu DanFeng [1 ]
机构
[1] Hefei Univ Technol, Sch Math, Hefei 230009, Peoples R China
基金
中国国家自然科学基金;
关键词
neuronal population; phase response curve; synaptic input; external periodic stimulus; OSCILLATIONS; NETWORKS;
D O I
10.1007/s11431-014-5532-2
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this study, we have formulated the phase description of the neuronal oscillator with non-instantaneous synaptic inputs and external periodic stimulus by using the phase sensitivity function. By numerical simulation, we have found that the phase of a neuronal oscillator undergoes periodic evolution or locked state, which is determined by the synaptic time constant. The synaptic time constant is also an important condition under which the global network is synchronized. When the synaptic time constant is relatively small, perfectly synchronized behavior quickly occurs in the neuronal population. As the synaptic time constant becomes slightly larger, periodic synchronization emerges in the neuronal population. However, synchronized activity in the neuronal population is lost for larger synaptic time constant. The external periodic stimulus can change the synchronization patterns in the neuronal population. With a weak low-frequency stimulus, the neuronal populations quick synchronized bursting; whereas a high-frequency stimulus can produce synchronized overlapping bursting. We have also found that neuronal oscillators with type-II phase response curves are more susceptible to synchronization than those with type-I phase response curves.
引用
收藏
页码:923 / 928
页数:6
相关论文
共 50 条
  • [1] Phase-response synchronization in neuronal population
    JIAO XianFa
    ZHU DanFeng
    Science China(Technological Sciences), 2014, (05) : 923 - 928
  • [2] Phase-response synchronization in neuronal population
    XianFa Jiao
    DanFeng Zhu
    Science China Technological Sciences, 2014, 57 : 923 - 928
  • [3] Synchronization in Neuronal Population with Phase Response
    Jiao, Xianfa
    Zhu, Danfeng
    Wang, Rubin
    ADVANCES IN COGNITIVE NEURODYNAMICS (IV), 2015, : 259 - 263
  • [4] Phase Response Synchronization in Neuronal Population with Time-Varying Coupling Strength
    Jiao, Xianfa
    Zhao, Wanyu
    Cao, Jinde
    COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2015, 2015
  • [5] How to correctly quantify neuronal phase-response curves from noisy recordings
    Hesse, Janina
    Schreiber, Susanne
    JOURNAL OF COMPUTATIONAL NEUROSCIENCE, 2019, 47 (01) : 17 - 30
  • [6] Phase-response curves of coupled oscillators
    Ko, Tae-Wook
    Ermentrout, G. Bard
    PHYSICAL REVIEW E, 2009, 79 (01)
  • [7] Stochastic Synchronization in Purkinje Cells with Feedforward Inhibition Could Be Studied with Equivalent Phase-Response Curves
    Verduzco-Flores, Sergio
    JOURNAL OF MATHEMATICAL NEUROSCIENCE, 2015, 5
  • [8] Efficient estimation of phase-response curves via compressive sensing
    Hong, Sungho
    Robberechts, Quinten
    De Schutter, Erik
    JOURNAL OF NEUROPHYSIOLOGY, 2012, 108 (07) : 2069 - 2081
  • [9] Phase-response curves and synchronized neural networks
    Smeal, Roy M.
    Ermentrout, G. Bard
    White, John A.
    PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 2010, 365 (1551) : 2407 - 2422
  • [10] Photic phase-response curves for cycling female mice
    Mizuta, Shuto
    Sugiyama, Mizuki
    Tokuda, Isao T.
    Nakamura, Wataru
    Nakamura, Takahiro J.
    HORMONES AND BEHAVIOR, 2018, 105 : 41 - 46