Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links.
Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links.
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DOI：
10.1038/srep00485
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发表时间：
2012
影响因子：
4.6
通讯作者：
Fukai, Tomoki
中科院分区：
文献类型：
--
作者：
Teramae, Jun-nosuke;Tsubo, Yasuhiro;Fukai, Tomoki
复杂网络的连通性和功能含义在许多物理、生物和社会系统中引起了人们的极大兴趣。然而，除了均匀加权或高斯加权的链路外，网络链路的权重分布的重要性在很大程度上仍然未知。在这里，我们从解析和数值上证明了递归神经网络可以通过递归连接的权值的长尾分布来稳健地产生对于神经元之间的尖峰信号传递最优的内部噪声。这种网络中自发活动的结构涉及弱-密集连接，它将兴奋活动作为噪声源重新分布在网络上，以最佳地增强单个神经元对稀疏-强连接输入的反应，从而打开多条信号传递路径。电生理实验证实了该模型支持的高度广泛的连接性光谱的重要性。我们的结果确定了一种简单的网络机制，通过高度不均匀的连接强度同时支持稳定性和最佳通信来产生内部噪声。
The connectivity of complex networks and functional implications has been attracting much interest in many physical, biological and social systems. However, the significance of the weight distributions of network links remains largely unknown except for uniformly- or Gaussian-weighted links. Here, we show analytically and numerically, that recurrent neural networks can robustly generate internal noise optimal for spike transmission between neurons with the help of a long-tailed distribution in the weights of recurrent connections. The structure of spontaneous activity in such networks involves weak-dense connections that redistribute excitatory activity over the network as noise sources to optimally enhance the responses of individual neurons to input at sparse-strong connections, thus opening multiple signal transmission pathways. Electrophysiological experiments confirm the importance of a highly broad connectivity spectrum supported by the model. Our results identify a simple network mechanism for internal noise generation by highly inhomogeneous connection strengths supporting both stability and optimal communication.
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影响因子：
64.8
作者：
Kenet, T;Bibitchkov, D;Arieli, A
通讯作者：
Arieli, A
DOI：
10.1073/pnas.0712231105
发表时间：
2008-03-04
影响因子：
11.1
作者：
Izhikevich, Eugene M.;Edelman, Gerald M.
通讯作者：
Edelman, Gerald M.
影响因子：
2.9
作者：
Fourcaud, N;Brunel, N
通讯作者：
Brunel, N
影响因子：
9.8
作者：
Hromádka T;Deweese MR;Zador AM
通讯作者：
Zador AM
影响因子：
5.3
作者：
de Rugy, Aymar;Loeb, Gerald E.;Carroll, Timothy J.
通讯作者：
Carroll, Timothy J.