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神经元振荡:不可避免且有用?

Neuronal oscillations: unavoidable and useful?

机构信息

Max Planck Institute for Brain Research (MPI), Frankfurt am Main, Germany.

Frankfurt Institute for Advanced Studies (FIAS), Frankfurt am Main, Germany.

出版信息

Eur J Neurosci. 2018 Oct;48(7):2389-2398. doi: 10.1111/ejn.13796. Epub 2018 Jan 22.

Abstract

Neuronal systems have a high propensity to engage in oscillatory activity because both the properties of individual neurons and canonical circuit motifs favour rhythmic activity. In addition, coupled oscillators can engage in a large variety of dynamical regimes, ranging from synchronization with different phase offsets to chaotic behaviour. Which regime prevails depends on differences between preferred oscillation frequencies, coupling strength and coupling delays. The ability of delay coupled oscillator networks to generate a rich repertoire of temporally structured activation sequences is exploited by central pattern generator networks for the control of movements. However, it is less clear whether temporal patterning of neuronal discharges also plays a role in cognitive processes. Here, it will be argued that the temporal patterning of neuronal discharges emerging from delay coupled oscillator networks plays a pivotal role in all instances in which selective relations have to be established between the responses of distributed assemblies of neurons. Examples are the dynamic formation of functional networks, the selective routing of activity in densely interconnected networks, the attention-dependent selection of sensory signals, the fast and context-dependent binding of responses for further joint processing in pattern recognition and the formation of associations by learning. Special consideration is given to arguments that challenge a functional role of oscillations and synchrony in cognition because of the volatile nature of these phenomena and recent evidence will be reviewed suggesting that this volatility is functionally advantageous.

摘要

神经元系统具有很强的产生振荡活动的倾向,因为单个神经元的特性和典型的电路模式都有利于节律性活动。此外,耦合振荡器可以参与多种动力学状态,从不同相位偏移的同步到混沌行为。哪种状态占主导地位取决于优先振荡频率、耦合强度和耦合延迟之间的差异。延迟耦合振荡器网络产生丰富的时间结构激活序列的能力被中央模式发生器网络用于控制运动。然而,神经元放电的时间模式是否也在认知过程中起作用还不太清楚。在这里,将认为延迟耦合振荡器网络中产生的神经元放电的时间模式在所有需要在分布式神经元集合的反应之间建立选择性关系的情况下起着关键作用。例如,功能网络的动态形成、密集互连网络中活动的选择性路由、依赖注意的感觉信号选择、快速且上下文相关的响应绑定以用于模式识别中的进一步联合处理以及通过学习形成关联。特别考虑了由于这些现象的不稳定性以及最近的证据表明这种不稳定性在功能上是有利的,因此对认为振荡和同步在认知中没有功能作用的论点进行了审查。

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