Dynamics of Spiking Neurons Connected by Both Inhibitory and Electrical Coupling

被引:0
|
作者
Timothy J. Lewis
John Rinzel
机构
[1] New York University,Center for Neural Science and Courant Institute for Mathematical Science
来源
关键词
synchrony; electrical coupling; gap junctions; inhibition;
D O I
暂无
中图分类号
学科分类号
摘要
We study the dynamics of a pair of intrinsically oscillating leaky integrate-and-fire neurons (identical and noise-free) connected by combinations of electrical and inhibitory coupling. We use the theory of weakly coupled oscillators to examine how synchronization patterns are influenced by cellular properties (intrinsic frequency and the strength of spikes) and coupling parameters (speed of synapses and coupling strengths). We find that, when inhibitory synapses are fast and the electrotonic effect of the suprathreshold portion of the spike is large, increasing the strength of weak electrical coupling promotes synchrony. Conversely, when inhibitory synapses are slow and the electrotonic effect of the suprathreshold portion of the spike is small, increasing the strength of weak electrical coupling promotes antisynchrony (see Fig. 10). Furthermore, our results indicate that, given a fixed total coupling strength, either electrical coupling alone or inhibition alone is better at enhancing neural synchrony than a combination of electrical and inhibitory coupling. We also show that these results extend to moderate coupling strengths.
引用
收藏
页码:283 / 309
页数:26
相关论文
共 50 条
  • [21] Synchronization of interacted spiking neuronal networks with inhibitory coupling
    Andreev, Andrey V.
    Maksimenko, Vladimir A.
    Pisarchik, Alexander N.
    Hramov, Alexander E.
    Chaos, Solitons and Fractals, 2021, 146
  • [22] Self-organization and segmentation with laterally connected spiking neurons
    Choe, YS
    Miikkulainen, R
    IJCAI-97 - PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2, 1997, : 1120 - 1125
  • [23] Modeling weakly connected networks of neural oscillators with spiking neurons
    Valova, I
    Gueorguieva, N
    Georgiev, G
    INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOL 1-4, PROCEEDINGS, 2005, : 810 - 815
  • [24] Collective and synchronous dynamics of photonic spiking neurons
    Takahiro Inagaki
    Kensuke Inaba
    Timothée Leleu
    Toshimori Honjo
    Takuya Ikuta
    Koji Enbutsu
    Takeshi Umeki
    Ryoichi Kasahara
    Kazuyuki Aihara
    Hiroki Takesue
    Nature Communications, 12
  • [25] Dynamics of spiking neurons: between homogeneity and synchrony
    Rangan, Aaditya V.
    Young, Lai-Sang
    JOURNAL OF COMPUTATIONAL NEUROSCIENCE, 2013, 34 (03) : 433 - 460
  • [26] Dynamics of spiking neurons: between homogeneity and synchrony
    Aaditya V. Rangan
    Lai-Sang Young
    Journal of Computational Neuroscience, 2013, 34 : 433 - 460
  • [27] Emergent dynamics of spiking neurons with fluctuating threshold
    Bhattacharjee, Anindita
    Das, M. K.
    COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2017, 46 : 126 - 134
  • [28] Collective and synchronous dynamics of photonic spiking neurons
    Inagaki, Takahiro
    Inaba, Kensuke
    Leleu, Timothee
    Honjo, Toshimori
    Ikuta, Takuya
    Enbutsu, Koji
    Umeki, Takeshi
    Kasahara, Ryoichi
    Aihara, Kazuyuki
    Takesue, Hiroki
    NATURE COMMUNICATIONS, 2021, 12 (01)
  • [29] Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons
    Yang, Geunbo
    Lee, Wongyu
    Seo, Youjung
    Lee, Choongseop
    Seok, Woojoon
    Park, Jongkil
    Sim, Donggyu
    Park, Cheolsoo
    SENSORS, 2023, 23 (16)
  • [30] ARE SINGLE RETINAL NEURONS BOTH EXCITATORY AND INHIBITORY
    MILLER, RF
    NATURE, 1988, 336 (6199) : 517 - 518