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The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models

机译:节奏状态在知觉学习中的作用:来自实验和计算模型的见解

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摘要

During the past two decades growing evidence indicates that brain oscillations in the alpha band (~10 Hz) not only reflect an “idle” state of cortical activity, but also take a more active role in the generation of complex cognitive functions. A recent study shows that more than 60% of the observed inter-subject variability in perceptual learning can be ascribed to ongoing alpha activity. This evidence indicates a significant role of alpha oscillations for perceptual learning and hence motivates to explore the potential underlying mechanisms. Hence, it is the purpose of this review to highlight existent evidence that ascribes intrinsic alpha oscillations a role in shaping our ability to learn. In the review, we disentangle the alpha rhythm into different neural signatures that control information processing within individual functional building blocks of perceptual learning. We further highlight computational studies that shed light on potential mechanisms regarding how alpha oscillations may modulate information transfer and connectivity changes relevant for learning. To enable testing of those model based hypotheses, we emphasize the need for multidisciplinary approaches combining assessment of behavior and multi-scale neuronal activity, active modulation of ongoing brain states and computational modeling to reveal the mathematical principles of the complex neuronal interactions. In particular we highlight the relevance of multi-scale modeling frameworks such as the one currently being developed by “The Virtual Brain” project.
机译:在过去的二十年中,越来越多的证据表明,α波段(〜10 Hz)的大脑振动不仅反映了皮质活动的“闲置”状态,而且在复杂的认知功能的产生中发挥了更为积极的作用。最近的一项研究表明,在感知学习中观察到的受试者间变异性的60%以上可归因于持续的alpha活动。该证据表明α振荡对于知觉学习具有重要作用,因此激发了探索潜在的潜在机制的动力。因此,本综述的目的是强调现有的证据,这些证据将内在的α振荡归因于塑造我们的学习能力。在这篇综述中,我们将阿尔法节律分解为不同的神经信号,这些信号控制知觉学习的各个功能构建块内的信息处理。我们还将重点介绍计算研究,这些研究揭示了有关α振荡如何调节信息传递和与学习相关的连接性变化的潜在机制。为了验证这些基于模型的假设,我们强调需要采取多学科方法,将行为和多尺度神经元活动的评估,正在进行的大脑状态的主动调制和计算模型相结合,以揭示复杂神经元相互作用的数学原理。我们特别强调了多尺度建模框架的相关性,例如“虚拟大脑”项目目前正在开发的框架。

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