Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV - Structuring synaptic pathways among recurrent connections
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV - Structuring synaptic pathways among recurrent connections
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DOI：
10.1007/s00422-009-0346-1
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发表时间：
2009-12-01
影响因子：
1.9
通讯作者：
van Hemmen, J. Leo
中科院分区：
文献类型：
--
作者：
Gilson, Matthieu;Burkitt, Anthony N.;van Hemmen, J. Leo
在神经元网络中，突触强度（或权重）的变化是由尖峰时间依赖的可塑性（STDP）引起的。本文研究了这种现象是如何发生的兴奋性经常性连接的网络与固定的输入权重，刺激外部尖峰列车。我们开发了一个理论框架的基础上的泊松神经元模型来分析神经元的活动（发射率和尖峰时间的相关性）和学习动力学之间的相互作用，当网络是由相关的池刺激均匀泊松尖峰列车。STDP可以导致所有神经元放电率的稳定（稳态平衡）和鲁棒的权重专业化。用于经常性权重的专门化的模式由输入发射率和相关结构、网络拓扑、STDP参数和突触响应特性之间的关系确定。我们发现前馈途径或加强自我反馈的领域出现在一个最初的同质经常性网络的条件。
In neuronal networks, the changes of synaptic strength (or weight) performed by spike-timing-dependent plasticity (STDP) are hypothesized to give rise to functional network structure. This article investigates how this phenomenon occurs for the excitatory recurrent connections of a network with fixed input weights that is stimulated by external spike trains. We develop a theoretical framework based on the Poisson neuron model to analyze the interplay between the neuronal activity (firing rates and the spike-time correlations) and the learning dynamics, when the network is stimulated by correlated pools of homogeneous Poisson spike trains. STDP can lead to both a stabilization of all the neuron firing rates (homeostatic equilibrium) and a robust weight specialization. The pattern of specialization for the recurrent weights is determined by a relationship between the input firing-rate and correlation structures, the network topology, the STDP parameters and the synaptic response properties. We find conditions for feed-forward pathways or areas with strengthened self-feedback to emerge in an initially homogeneous recurrent network.