A model for representing the dynamics of a system of synfire chains

被引:37
|
作者
Hayon, G
Abeles, M [1 ]
Lehmann, D
机构
[1] Hebrew Univ Jerusalem, Dept Physiol, Jerusalem, Israel
[2] Hebrew Univ Jerusalem, Ctr Neural Computat, Jerusalem, Israel
[3] Bar Ilan Univ, Gonda Brain Res Ctr, Ramat Gan, Israel
[4] Hebrew Univ Jerusalem, Dept Comp Sci, IL-91904 Jerusalem, Israel
关键词
synfire-chains; compositionality; binding-mechanism; neural-networks;
D O I
10.1007/s10827-005-5479-1
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Competitive synchronization among synfire chains may model the dynamics of binding and compositionality. Typically, such models require simulations of hundreds of thousands of neurons. Here we show that the behavior of such large systems can be numerically analyzed by representing the neuronal activity in a synfire chain as a wave. The position and velocity of waves are the only parameters needed to represent the neural activity within a synfire chain. With this wave model we describe how waves are generated, decay, interact within a single chain and among chains. The behavior of the wave model is compared to the behavior of detailed simulations of synfire chains with no qualitative difference. We show that interacting waves tend to become locked to each other (wave synchronization). Finally we prove that: (1) Within a system of many synfire chains with symmetric interchain connections, as long as waves do not fade away or become fully synchronized, the total synchrony among waves can only increase (or stay constant), but never decrease. (2) A wave that increases its speed during the synchronization process becomes more stable.
引用
收藏
页码:41 / 53
页数:13
相关论文
共 50 条
  • [1] A Model for Representing the Dynamics of a System of Synfire Chains
    Gaby Hayon
    Moshe Abeles
    Daniel Lehmann
    Journal of Computational Neuroscience, 2005, 18 : 41 - 53
  • [2] ANALYSIS OF SYNFIRE CHAINS
    HERRMANN, M
    HERTZ, JA
    PRUGELBENNETT, A
    NETWORK-COMPUTATION IN NEURAL SYSTEMS, 1995, 6 (03) : 403 - 414
  • [3] A learning algorithm for synfire chains
    Sougné, J
    CONNECTIONIST MODELS OF LEARNING, DEVELOPMENT AND EVOLUTION, 2000, : 23 - 32
  • [4] Synfire chains in a balanced network
    Aviel, Y
    Pavlov, E
    Abeles, M
    Horn, D
    8TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING, VOLS 1-3, PROCEEDING, 2001, : 1431 - 1433
  • [5] Synchronization and rate dynamics in embedded synfire chains: effect of network heterogeneity and feedback
    Tom Tetzlaff
    Gaute T Einevoll
    Markus Diesmann
    BMC Neuroscience, 10 (Suppl 1)
  • [6] High-capacity embedding of synfire chains in a cortical network model
    Chris Trengove
    Cees van Leeuwen
    Markus Diesmann
    Journal of Computational Neuroscience, 2013, 34 : 185 - 209
  • [7] Analysis of synfire chains above saturation
    Reyes, RM
    Vicente, CJP
    BIOLOGICAL AND ARTIFICIAL COMPUTATION: FROM NEUROSCIENCE TO TECHNOLOGY, 1997, 1240 : 162 - 168
  • [8] On embedding synfire chains in a balanced network
    Aviel, Y
    Mehring, C
    Abeles, M
    Horn, D
    NEURAL COMPUTATION, 2003, 15 (06) : 1321 - 1340
  • [9] Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains
    Wang, Zhuo
    Sornborger, Andrew T.
    Tao, Louis
    PLOS COMPUTATIONAL BIOLOGY, 2016, 12 (06)
  • [10] High-capacity embedding of synfire chains in a cortical network model
    Trengove, Chris
    van Leeuwen, Cees
    Diesmann, Markus
    JOURNAL OF COMPUTATIONAL NEUROSCIENCE, 2013, 34 (02) : 185 - 209