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A Canonical Circuit for Generating Phase-Amplitude Coupling

机译:产生相位-幅度耦合的规范电路

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摘要

‘Phase amplitude coupling’ (PAC) in oscillatory neural activity describes a phenomenon whereby the amplitude of higher frequency activity is modulated by the phase of lower frequency activity. Such coupled oscillatory activity – also referred to as ‘cross-frequency coupling’ or ‘nested rhythms’ – has been shown to occur in a number of brain regions and at behaviorally relevant time points during cognitive tasks; this suggests functional relevance, but the circuit mechanisms of PAC generation remain unclear. In this paper we present a model of a canonical circuit for generating PAC activity, showing how interconnected excitatory and inhibitory neural populations can be periodically shifted in to and out of oscillatory firing patterns by afferent drive, hence generating higher frequency oscillations phase-locked to a lower frequency, oscillating input signal. Since many brain regions contain mutually connected excitatory-inhibitory populations receiving oscillatory input, the simplicity of the mechanism generating PAC in such networks may explain the ubiquity of PAC across diverse neural systems and behaviors. Analytic treatment of this circuit as a nonlinear dynamical system demonstrates how connection strengths and inputs to the populations can be varied in order to change the extent and nature of PAC activity, importantly which phase of the lower frequency rhythm the higher frequency activity is locked to. Consequently, this model can inform attempts to associate distinct types of PAC with different network topologies and physiologies in real data.
机译:振荡神经活动中的“相位幅度耦合”(PAC)描述了一种现象,高频活动的幅度受低频活动的相位调制。这种耦合的振荡活动(也称为“跨频耦合”或“嵌套节律​​”)已显示在认知任务过程中的许多大脑区域和行为相关的时间点上;这表明功能相关,但PAC产生的电路机制仍不清楚。在本文中,我们介绍了一种用于产生PAC活动的规范电路模型,该模型展示了如何通过传入驱动器周期性地将相互关联的兴奋性和抑制性神经种群移入和移出振荡激发模式,从而产生更高频率的振荡,并将其锁相较低的频率,振荡输入信号。由于许多大脑区域包含接收振荡输入的相互连接的兴奋抑制种群,因此在此类网络中生成PAC的机制的简单性可以解释PAC在各种神经系统和行为中的普遍性。该电路作为非线性动力学系统的分析处理表明,如何改变连接强度和与总体的输入,以改变PAC活动的程度和性质,重要的是,将较高频率活动锁定在较低频率节奏的哪个阶段。因此,该模型可以为尝试将不同类型的PAC与实际数据中的不同网络拓扑和生理相关联提供信息。

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