Synchronization in a Network of Spiking Neural Oscillators with Plastic Connectivity

被引:8
|
作者
Bazhanova, M. V. [1 ]
Krylova, N. P. [1 ]
Kazantsev, V. B. [1 ,2 ,3 ]
Khramov, A. E. [1 ,2 ]
Lobov, S. A. [1 ,2 ]
机构
[1] NI Lobachevsky State Univ Nizhny Novgorod, Inst Biol & Biomed, Nizhnii Novgorod, Russia
[2] Univ Innopolis, Ctr Technol Robot & Mechatron Components, Innopolis, Russia
[3] Samara State Med Univ, Samara, Russia
基金
俄罗斯基础研究基金会;
关键词
Neurons - Brain - Neural networks;
D O I
10.1007/s11141-021-10054-2
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Synchronization of neural activity plays an important role for processing of information in the brain. In this work, we study the dynamics of a neural network with plastic synaptic connections under the action of a spatially localized stimulus. It is established that synchronization of all the network neurons by a local action is possible in a certain range of periodic stimulation. The network forms the signals in the form of quasi-synchronous bursts of pulses and synchronization of the bursts with the applied stimuli occurs under an external periodic action. It is shown that the network synchronization takes place in a certain frequency range of external action. An extension of this range is observed with increasing geometric size of the stimulated region. The synchronization range decreases with increasing number of the neuron connections in the network. It is also established that the plastic (adaptive) connections among the elements of the network can improve its sensitivity to external action, i.e., extend the frequency range of stimulation which causes the network synchronization, and decrease the minimum size of the network segment exposed to external action.
引用
收藏
页码:298 / 309
页数:12
相关论文
共 50 条
  • [21] A SIMPLE DIGITAL SPIKING NEURAL NETWORK: SYNCHRONIZATION AND SPIKE-TRAIN APPROXIMATION
    Uchida, Hiroaki
    Oishi, Yuya
    Saito, Toshimichi
    DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES S, 2021, 14 (04): : 1479 - 1494
  • [22] Achieving Swarm Intelligence with Spiking Neural Oscillators
    Fang, Yan
    Dickerson, Samuel J.
    2017 IEEE INTERNATIONAL CONFERENCE ON REBOOTING COMPUTING (ICRC), 2017, : 16 - 19
  • [23] Chaotic Spiking Neural Network Connectivity Configuration Leading to Memory Mechanism Formation
    Kiselev, Mikhail
    ADVANCES IN NEURAL COMPUTATION, MACHINE LEARNING, AND COGNITIVE RESEARCH III, 2020, 856 : 398 - 404
  • [24] Anisotropic connectivity implements motion-based prediction in a spiking neural network
    Kaplan, Bernhard A.
    Lansner, Anders
    Masson, Guillaume S.
    Perrinet, Laurent U.
    FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2013, 7
  • [25] Synchronization of Three Coupled Plastic Bottle Oscillators
    Kohira, Masahiro I.
    Magome, Nobuyuki
    Mouri, Shin-Ichiro
    Kitahata, Hiroyuki
    Yoshikawa, Kenichi
    INTERNATIONAL JOURNAL OF UNCONVENTIONAL COMPUTING, 2009, 5 (01) : 103 - 111
  • [26] Synchronization of locally coupled neural oscillators
    Dragoi, V
    Grosu, I
    NEURAL PROCESSING LETTERS, 1998, 7 (03) : 199 - 210
  • [27] Synchronization of Locally Coupled Neural Oscillators
    Valentin Dragoi
    Ioan Grosu
    Neural Processing Letters, 1998, 7 : 199 - 210
  • [28] Synchronization of Duffing-Holmes Oscillators Using Stable Neural Network Controller
    Kuntanapreeda, Suwat
    COMPUTATIONAL COLLECTIVE INTELLIGENCE: TECHNOLOGIES AND APPLICATIONS, PT III, 2010, 6423 : 242 - 251
  • [29] Spiking neural network connectivity and its potential for temporal sensory processing and variable binding
    Wall, Julie
    Glackin, Cornelius
    FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2013, 7
  • [30] Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity
    Okatan, M
    Wilson, MA
    Brown, EN
    NEURAL COMPUTATION, 2005, 17 (09) : 1927 - 1961