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具有结构连接的递归神经网络中的相干混沌。

Coherent chaos in a recurrent neural network with structured connectivity.

机构信息

Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.

Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America.

出版信息

PLoS Comput Biol. 2018 Dec 13;14(12):e1006309. doi: 10.1371/journal.pcbi.1006309. eCollection 2018 Dec.

Abstract

We present a simple model for coherent, spatially correlated chaos in a recurrent neural network. Networks of randomly connected neurons exhibit chaotic fluctuations and have been studied as a model for capturing the temporal variability of cortical activity. The dynamics generated by such networks, however, are spatially uncorrelated and do not generate coherent fluctuations, which are commonly observed across spatial scales of the neocortex. In our model we introduce a structured component of connectivity, in addition to random connections, which effectively embeds a feedforward structure via unidirectional coupling between a pair of orthogonal modes. Local fluctuations driven by the random connectivity are summed by an output mode and drive coherent activity along an input mode. The orthogonality between input and output mode preserves chaotic fluctuations by preventing feedback loops. In the regime of weak structured connectivity we apply a perturbative approach to solve the dynamic mean-field equations, showing that in this regime coherent fluctuations are driven passively by the chaos of local residual fluctuations. When we introduce a row balance constraint on the random connectivity, stronger structured connectivity puts the network in a distinct dynamical regime of self-tuned coherent chaos. In this regime the coherent component of the dynamics self-adjusts intermittently to yield periods of slow, highly coherent chaos. The dynamics display longer time-scales and switching-like activity. We show how in this regime the dynamics depend qualitatively on the particular realization of the connectivity matrix: a complex leading eigenvalue can yield coherent oscillatory chaos while a real leading eigenvalue can yield chaos with broken symmetry. The level of coherence grows with increasing strength of structured connectivity until the dynamics are almost entirely constrained to a single spatial mode. We examine the effects of network-size scaling and show that these results are not finite-size effects. Finally, we show that in the regime of weak structured connectivity, coherent chaos emerges also for a generalized structured connectivity with multiple input-output modes.

摘要

我们提出了一个简单的模型,用于在递归神经网络中实现相干的、空间相关的混沌。随机连接的神经元网络表现出混沌波动,已被研究作为捕获皮质活动时间变化的模型。然而,这种网络产生的动力学是空间上不相关的,不会产生相干波动,而相干波动是在新皮质的多个空间尺度上普遍观察到的。在我们的模型中,我们引入了一种结构化的连接成分,除了随机连接之外,还通过一对正交模式之间的单向耦合有效地嵌入了前馈结构。由随机连接驱动的局部波动由输出模式求和,并沿着输入模式驱动相干活动。输入和输出模式之间的正交性通过防止反馈环来保持混沌波动。在弱结构连接的情况下,我们应用微扰方法来求解动态平均场方程,表明在这种情况下,相干波动是由局部残余波动的混沌被动驱动的。当我们在随机连接上引入行平衡约束时,更强的结构连接将网络置于一个独特的自调谐相干混沌动力学状态。在这种情况下,动力学的相干分量间歇性地自我调整,以产生缓慢、高度相干的混沌周期。动力学表现出更长的时间尺度和类似开关的活动。我们展示了在这种情况下,动力学如何定性地取决于连接矩阵的特定实现:一个复杂的主导特征值可以产生相干振荡混沌,而一个实的主导特征值可以产生具有破对称的混沌。相干性的水平随着结构连接强度的增加而增加,直到动力学几乎完全被限制在单个空间模式中。我们研究了网络大小缩放的影响,并表明这些结果不是有限大小效应。最后,我们表明在弱结构连接的情况下,对于具有多个输入输出模式的广义结构连接,相干混沌也会出现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4caa/6307850/885506fb6e22/pcbi.1006309.g001.jpg

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