Synchronization of Electrically Coupled Resonate-and-Fire Neurons

被引:0
|
作者
Chartrand, Thomas [1 ,2 ]
Goldman, Mark S. [1 ,3 ]
Lewis, Timothy J. [1 ,4 ]
机构
[1] Univ Calif Davis, Grad Grp Appl Math, Davis, CA 95616 USA
[2] Allen Inst Brain Sci, Seattle, WA 98109 USA
[3] Univ Calif Davis, Dept Neurobiol Physiol & Behav, Dept Ophthalmol & Vis Sci, Ctr Neurosci, Davis, CA 95616 USA
[4] Univ Calif Davis, Dept Math, Davis, CA 95616 USA
来源
关键词
resonate-and-fire model; synchronization; electrical coupling; gap junction; phase response curve; hybrid model; PHASE-RESPONSE CURVES; SPIKING NEURONS; CHIMERA STATES; FIRING-RATE; IN-VITRO; MODEL; DYNAMICS; OSCILLATORS; FREQUENCY; EXCITATION;
D O I
10.1137/18M1197412
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Electrical coupling between neurons is broadly present across brain areas and is typically assumed to synchronize network activity. However, intrinsic properties of the coupled cells can complicate this simple picture. Many cell types with electrical coupling show a diversity of post-spike subthreshold fluctuations, often linked to subthreshold resonance, which are transmitted through electrical synapses in addition to action potentials. Using the theory of weakly coupled oscillators, we explore the effect of both subthreshold and spike-mediated coupling on synchrony in small networks of electrically coupled resonate-and-fire neurons, a hybrid neuron model with damped subthreshold oscillations and a range of post-spike voltage dynamics. We calculate the phase response curve using an extension of the adjoint method that accounts for the discontinuous post-spike reset rule. We find that both spikes and subthreshold fluctuations can jointly promote synchronization. The subthreshold contribution is strongest when the voltage exhibits a significant post-spike elevation in voltage, or plateau potential. Additionally, we show that the geometry of trajectories approaching the spiking threshold causes a "reset-induced shear" effect that can oppose synchrony in the presence of network asymmetry, despite having no effect on the phase-locking of symmetrically coupled pairs.
引用
收藏
页码:1643 / 1693
页数:51
相关论文
共 50 条
  • [1] Resonate-and-fire neurons
    Izhikevich, EM
    NEURAL NETWORKS, 2001, 14 (6-7) : 883 - 894
  • [2] Burst synchronization and chaotic phenomena in two strongly coupled resonate-and-fire neurons
    Nakada, Kazuki
    Miura, Keiji
    Hayashi, Hatsuo
    INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS, 2008, 18 (04): : 1249 - 1259
  • [3] Theoretical analysis of synchronization phenomena in two pulse-coupled resonate-and-fire neurons
    Nakada, Kazuki
    Miura, Keiji
    Hayashi, Hatsuo
    2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6, 2007, : 950 - +
  • [4] Globally coupled resonate-and-fire models
    Miura, K
    Okada, M
    PROGRESS OF THEORETICAL PHYSICS SUPPLEMENT, 2006, (161): : 255 - 259
  • [5] Resonate-and-Fire Neurons for Radar Interference Detection
    Hille, Julian
    Auge, Daniel
    Grassmann, Cyprian
    Knoll, Alois
    PROCEEDINGS OF INTERNATIONAL CONFERENCE ON NEUROMORPHIC SYSTEMS 2022, ICONS 2022, 2022,
  • [6] Burst synchronization in two pulse-coupled resonate-and-fire neuron circuits
    Nakada, Kazuki
    Asai, Tetsuya
    Hayashi, Hatsuo
    PROFESSIONAL PRACTICE IN ARTIFICIAL INTELLIGENCE, 2006, 218 : 285 - +
  • [7] Pulse-coupled resonate-and-fire models
    Miura, Keiji
    Okada, Masato
    ELECTRONICS AND COMMUNICATIONS IN JAPAN PART II-ELECTRONICS, 2006, 89 (07): : 21 - 28
  • [8] Pulse-coupled resonate-and-fire models
    Miura, K
    Okada, M
    PHYSICAL REVIEW E, 2004, 70 (02): : 7
  • [9] Direct Signal Encoding With Analog Resonate-and-Fire Neurons
    Lehmann, Hendrik M.
    Hille, Julian
    Grassmann, Cyprian
    Issakov, Vadim
    IEEE ACCESS, 2023, 11 : 50052 - 50063
  • [10] Synchronization Analysis of Resonate-and-Fire Neuron Models with Delayed Resets
    Miura, Keiji
    Nakada, Kazuki
    6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS, 2012, : 1076 - 1079