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题名: Spike Pattern Structure Influences Synaptic Efficacy Variability under STDP and Synaptic Homeostasis. I: Spike Generating Models on Converging Motifs
作者: Bi, ZD ;  Zhou, CS
刊名: FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
出版日期: 2016
卷号: 10, 页码:14
关键词: spike pattern structure ;  synaptic plasticity ;  efficacy variability ;  STDP ;  synaptic homeostasis ;  spike generating models
学科分类: Mathematical & Computational Biology; Neurosciences & Neurology
DOI: http://dx.doi.org/10.3389/fncom.2016.00014
通讯作者: Bi, ZD (reprint author), Chinese Acad Sci, Inst Theoret Phys, State Key Lab Theoret Phys, Beijing 100080, Peoples R China. ;  Bi, ZD ;  Zhou, CS (reprint author), Hong Kong Baptist Univ, Dept Phys, Kowloon Tong, Hong Kong, Peoples R China. ;  Zhou, CS (reprint author), Hong Kong Baptist Univ, Inst Computat & Theoret Studies, Beijing Hong Kong Singapore Joint Ctr Nonlinear &, Ctr Nonlinear Studies, Kowloon Tong, Hong Kong, Peoples R China. ;  Zhou, CS (reprint author), Beijing Computat Sci Res Ctr, Beijing, Peoples R China. ;  Zhou, CS (reprint author), HKBU Inst Res & Continuing Educ, Res Ctr, Shenzhen, Peoples R China.
文章类型: Article
英文摘要: In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons and synapses as well as the randomness of connection details, spike trains typically exhibit variability such as spatial randomness and temporal stochasticity, resulting in variability of synaptic changes under plasticity, which we call efficacy variability. How the variability of spike trains influences the efficacy variability of synapses remains unclear. In this paper, we try to understand this influence under pair-wise additive spike-timing dependent plasticity (STDP) when the mean strength of plastic synapses into a neuron is bounded (synaptic homeostasis). Specifically, we systematically study, analytically and numerically, how four aspects of statistical features, i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations, as well as their interactions influence the efficacy variability in converging motifs (simple networks in which one neuron receives from many other neurons). Neurons (including the post-synaptic neuron) in a converging motif generate spikes according to statistical models with tunable parameters. In this way, we can explicitly control the statistics of the spike patterns, and investigate their influence onto the efficacy variability, without worrying about the feedback from synaptic changes onto the dynamics of the post-synaptic neuron. We separate efficacy variability into two parts: the drift part (DriftV) induced by the heterogeneity of change rates of different synapses, and the diffusion part (Ditty) induced by weight diffusion caused by stochasticity of spike trains. Our main findings are: (1) synchronous firing and burstiness tend to increase DiffV, (2) heterogeneity of rates induces DriftV when potentiation and depression in STDP are not balanced, and (3) heterogeneity of cross correlations induces DriftV together with heterogeneity of rates. We anticipate our work important for understanding functional processes of neuronal networks (such as memory) and neural development.
类目[WOS]: Mathematical & Computational Biology ;  Neurosciences
关键词[WOS]: TIMING-DEPENDENT PLASTICITY ;  CROSS-CORRELATION ;  WORKING-MEMORY ;  NEURONAL AVALANCHES ;  CORTICAL-NEURONS ;  REGULAR SPIKING ;  NEURAL ACTIVITY ;  BARREL CORTEX ;  LEARNING RULE ;  FIRING RATES
收录类别: SCI
项目资助者: Hong Kong Baptist University (IIKBU) Strategic Development Fund ;  NSFC-RGC Joint Research Scheme [HKUST/NSFC/12-13/01] ;  NSFC [11275027]
语种: 英语
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内容类型: 期刊论文
URI标识: http://ir.itp.ac.cn/handle/311006/21757
Appears in Collections:理论物理所2016年知识产出_期刊论文

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Recommended Citation:
Bi, ZD,Zhou, CS. Spike Pattern Structure Influences Synaptic Efficacy Variability under STDP and Synaptic Homeostasis. I: Spike Generating Models on Converging Motifs[J]. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,2016,10:14.
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