Eigenvalue spectra of random matrices for neural networks
Eigenvalue spectra of random matrices for neural networks
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DOI：
10.1103/physrevlett.97.188104
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发表时间：
2006-11-03
影响因子：
8.6
通讯作者：
Abbott, L. F.
中科院分区：
文献类型：
--
作者：
Rajan, Kanaka;Abbott, L. F.
神经网络的动力学受到描述其突触连接的矩阵的特征值谱的强烈影响。在大型网络中，突触连接矩阵的元素可以从适当的分布中随机选择，使得随机矩阵理论的结果高度相关。不幸的是，随机矩阵的特征值谱的经典结果不适用于突触连接矩阵，因为单个神经元是兴奋性或抑制性的约束。因此，我们计算的大随机矩阵的特征值谱的兴奋性和抑制性列从分布具有不同的手段和相同或不同的方差。
The dynamics of neural networks is influenced strongly by the spectrum of eigenvalues of the matrix describing their synaptic connectivity. In large networks, elements of the synaptic connectivity matrix can be chosen randomly from appropriate distributions, making results from random matrix theory highly relevant. Unfortunately, classic results on the eigenvalue spectra of random matrices do not apply to synaptic connectivity matrices because of the constraint that individual neurons are either excitatory or inhibitory. Therefore, we compute eigenvalue spectra of large random matrices with excitatory and inhibitory columns drawn from distributions with different means and equal or different variances.