Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV - Structuring synaptic pathways among recurrent connections

被引:41
作者
Gilson, Matthieu [1 ,2 ,3 ]
Burkitt, Anthony N. [1 ,2 ,3 ]
Grayden, David B. [1 ,2 ,3 ]
Thomas, Doreen A. [1 ,3 ]
van Hemmen, J. Leo [4 ,5 ]
机构
[1] Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic 3010, Australia
[2] Bion Ear Inst, Melbourne, Vic 3002, Australia
[3] Univ Melbourne, Victoria Res Lab, NICTA, Melbourne, Vic 3010, Australia
[4] Tech Univ Munich, Phys Dept T35, D-85747 Garching, Germany
[5] Tech Univ Munich, BCCN Munich, D-85747 Garching, Germany
基金
澳大利亚研究理事会;
关键词
Learning; STDP; Recurrent neuronal network; Spike-time correlation; DYNAMICS; INPUT; MODEL; SYNCHRONY; DELAYS; POINT;
D O I
10.1007/s00422-009-0346-1
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:427 / 444
页数:18
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