Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Department of Neurology, Charité University Medicine Berlin Berlin, Germany ; Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; Berlin School of Mind and Brain & Mind and Brain Institute, Humboldt-Universität zu Berlin Berlin, Germany.
Department of Medical Psychology and Behavioral Neurobiology & Center for Integrative Neuroscience (CIN), University of Tübingen Tübingen, Germany.
Front Comput Neurosci. 2015 Feb 26;9:1. doi: 10.3389/fncom.2015.00001. eCollection 2015.
Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous "spontaneous" shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly.
学习是一种复杂的大脑功能,在不同的时间尺度上运作,从毫秒到数年,它会引起大脑动态的持久变化。大脑也会不断地经历状态的“自发”转变,其中包括各种频率的节律性活动。除了最明显的清醒和睡眠两种状态外,与清醒相关的大脑状态还包括警觉性和注意力的调节。最近的发现表明,某些大脑状态,特别是在睡眠期间,对于学习和记忆的巩固是必不可少的。振荡活动在多个空间尺度上起着至关重要的作用,例如在突触水平的可塑性或在大脑区域之间的通信中。然而,将大脑状态和节律与学习联系起来的潜在机制和计算规则,尽管对于我们理解大脑功能和大脑疾病的治疗方法很重要,但尚未阐明。在这里,我们回顾了已知的大脑状态通过其特征性的节律特征来介导和调节学习的机制。为了理解大脑状态、大脑节律和学习过程之间的关键相互作用,需要整合来自动物模型和人类受试者的从单个突触到大规模皮质水平的广泛的实验和理论工作。通过讨论实验和理论方法的结果,我们为利用神经元学习机制来开发工具和治疗方法指明了新的途径,例如用于中风患者的治疗以及为老年人设计记忆增强策略。