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Coherent chaos in a recurrent neural network with structured connectivity

机译:具有结构化连通性的递归神经网络中的相干混沌

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摘要

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.
机译:我们提出了一个简单的模型,用于递归神经网络中相干,空间相关的混沌。随机连接的神经元网络表现出混沌波动,并已作为捕捉皮层活动时间变化的模型进行了研究。然而,由这种网络产生的动力学在空间上是不相关的,并且不会产生连贯的波动,这通常是在新皮层的空间尺度上观察到的。在我们的模型中,除了随机连接之外,我们还引入了连接的结构化组件,该组件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。由随机连通性驱动的局部波动由输出模式求和,并沿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路来保留混沌波动。在弱结构连通性的情况下,我们采用微扰方法来求解动态平均场方程,这表明在这种情况下,相干波动是由局部残余波动的混沌被动地驱动的。当我们在随机连通性上引入行平衡约束时,更强的结构化连通性使网络处于自调整相干混沌的独特动态状态。在这种情况下,动力学的相干部分会间歇性地自我调整,以产生缓慢,高度相干的混沌周期。动态显示更长的时间范围和类似开关的活动。我们展示了在这种情况下动力学如何定性地取决于连通性矩阵的特定实现:复杂的前导特征值可以产生相干的振荡混沌,而真正的前导特征值可以产生对称性破坏的混沌。相干性水平随着结构化连接强度的增加而增长,直到动力学几乎完全被限制为单个空间模式为止。我们检查了网络大小缩放的影响,并表明这些结果不是有限大小的影响。最后,我们表明,在弱结构化连接的情况下,对于具有多个输入-输出模式的广义结构化连接,也出现了连贯的混乱。

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