Stationary States in Infinite Networks of Spiking Oscillators with Noise

被引:3
|
作者
Louca, Stilianos [1 ]
Atay, Fatihcan M. [1 ]
机构
[1] Max Planck Inst Math Sci, D-04103 Leipzig, Germany
来源
SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS | 2013年 / 12卷 / 01期
关键词
Winfree model; coupled oscillators; Fokker-Planck equation; stationary state; noise; stability; KURAMOTO MODEL; SYNCHRONIZATION; POPULATION; STABILITY; INCOHERENCE; DYNAMICS;
D O I
10.1137/120880264
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
We model networks of identical, all-to-all, pulse-coupled phase oscillators with white noise, in the limit of infinite network size and Dirac pulses, using a Fokker-Planck equation for the phase probability density. We give analytical, constructive existence and uniqueness results for stationary states (i.e., time-independent densities) and derive and study a one-dimensional eigenvalue equation for their linear stability. Our results are supplemented by numerical methods, which are applied to two classes of oscillator response functions. We find that the stationary network activity depends for some response functions monotonically and for others nonmonotonically on the coupling and noise strength. In all cases we find that a sufficiently strong noise locally stabilizes the stationary state, and simulations suggest this stability to be global. For most response functions the stationary state undergoes a supercritical Hopf bifurcation as noise is decreased, and a locally stable limit cycle emerges in its proximity. On that limit cycle, the network splits into groups of approximately synchronized oscillators, while the network's (mean) activity oscillates at frequencies often much higher than the intrinsic oscillator frequency.
引用
收藏
页码:415 / 449
页数:35
相关论文
共 50 条
  • [31] STABLE AND UNSTABLE PERIODIC ORBITS IN COMPLEX NETWORKS OF SPIKING NEURONS WITH DELAYS
    Memmesheimer, Raoul-Martin
    Timme, Marc
    DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS, 2010, 28 (04) : 1555 - 1588
  • [32] Reconfigurable Computation in Spiking Neural Networks
    Neves, Fabio Schittler
    Timme, Marc
    IEEE ACCESS, 2020, 8 : 179648 - 179655
  • [33] Chimeras in random non-complete networks of phase oscillators
    Laing, Carlo R.
    Rajendran, Karthikeyan
    Kevrekidis, Ioannis G.
    CHAOS, 2012, 22 (01)
  • [34] Stability in the Kuramoto–Sakaguchi model for finite networks of identical oscillators
    Antonio Mihara
    Rene O. Medrano-T
    Nonlinear Dynamics, 2019, 98 : 539 - 550
  • [35] Partial Phase Cohesiveness in Networks of Networks of Kuramoto Oscillators
    Qin, Yuzhen
    Kawano, Yu
    Portoles, Oscar
    Cao, Ming
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2021, 66 (12) : 6100 - 6107
  • [36] Impacts of clustering on noise-induced spiking regularity in the excitatory neuronal networks of subnetworks
    Li, Huiyan
    Sun, Xiaojuan
    Xiao, Jinghua
    FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2015, 9
  • [37] Improving the Stability for Spiking Neural Networks Using Anti-noise Learning Rule
    Luo, Yuling
    Fu, Qiang
    Liu, Junxiu
    Huang, Yongchuang
    Ding, Xuemei
    Cao, Yi
    PRICAI 2018: TRENDS IN ARTIFICIAL INTELLIGENCE, PT II, 2018, 11013 : 29 - 37
  • [38] Chimera states in networks of Van der Pol oscillators with hierarchical connectivities
    Ulonska, Stefan
    Omelchenko, Iryna
    Zakharova, Anna
    Schoell, Eckehard
    CHAOS, 2016, 26 (09)
  • [39] Spiking sychronization regulated by noise in three types of Hodgkin-Huxley neuronal networks
    Zhang Zheng-Zhen
    Zeng Shang-You
    Tang Wen-Yan
    Hu Jin-Lin
    Zeng Shao-Wen
    Ning Wei-Lian
    Qiu Yi
    Wu Hui-Si
    CHINESE PHYSICS B, 2012, 21 (10)
  • [40] Inverse stochastic resonance in networks of spiking neurons
    Uzuntarla, Muhammet
    Barreto, Ernest
    Torres, Joaquin J.
    PLOS COMPUTATIONAL BIOLOGY, 2017, 13 (07)