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arXiv 1301.3779cond-mat.dis-nn
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临界附近随机神经网络复杂动力学的最优系统规模

Optimal system size for complex dynamics in random neural networks near criticality

Gilles Wainrib, Luis Carlos García del Molino

摘要:

在这封信中,我们考虑通过随机连接矩阵耦合的动态代理模型,如[Sompolinsky et. 1988年,在随机神经网络的背景下。已知增加无序参数会引起导致混沌动力学的相变。我们在这里观察和研究一种新的现象在亚临界制度:观察复杂的动力学的概率是最大的中间系统的大小时,混乱是足够接近临界。我们给出了一个更一般的解释,这种类型的系统的尺寸共振的随机矩阵的特征值的极值理论的框架。

英文摘要:

In this Letter, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced in [Sompolinsky et. al, 1988] in the context of random neural networks. It is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the subcritical regime : the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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