Role of coordinated multi-area reactivations during transitions between automatic and flexible behaviors.

摘要 睡眠占据了我们生活的很大一部分，并被广泛认为执行基本功能。期间 睡眠时，神经元的参与规则和种群动力学与清醒时明显不同。 有大量证据表明，睡眠的一个主要功能是巩固在睡眠期间形成的记忆。 醒来然而，最近的研究表明，睡眠也可能积极地改变神经连接， 忘记（“unlearning”）大脑是如何平衡学习和遗忘的， 贡献，对合奏和行为的最终影响是未知的。 本提案的目标是确定在非快速眼- 运动睡眠（NREMS）实现学习与遗忘。我们的目标是研究 在NREMS过程中，调节皮层和纹状体之间的耦合，神经元的试验间变异性， 集合（“表征方差”），从而行为的自动性与灵活性。 自动性-根据上下文驱动的、快速而准确的行动的一致性生产-是 与皮质-纹状体偶联增加和可靠的整体活动（低方差）相关。在 相反，较弱的皮质-纹状体耦合与灵活，探索性状态相关， 代表性方差 我们将使用大鼠模型的运动技能学习（达到掌握），连同多部位 电生理学，识别特定的神经元亚型，闭环扰动，和详细的 计算机建模我们将确定具体的NREMS动态差异调节 重新激活神经元集合，以实现不同形式的活动依赖性可塑性。基于 广泛的初步数据，我们假设，全球缓慢振荡耦合纺锤触发 在皮质和纹状体亚区之间广泛协调的全局协调再激活。 这些协调的再激活加强了功能联系，并产生了更早的“直接”放电。 通路的纹状体神经元，促进自律性。相比之下，在delta期间发生的局部向上状态 波产生不协调的再激活，导致皮质纹状体的功能解耦 连接和“间接通路”纹状体神经元的早期放电，以促进探索和灵活性。 我们紧密结合的电生理学，光遗传学和计算研究将提供 新的机制的见解，正是如何在睡眠期间的大脑动力学实现巩固 以及对特定行为模式的遗忘。这些知识有很大的潜力帮助我们更好地 了解和治疗与之相关的广泛的神经和精神疾病 皮质-纹状体处理的改变。

Abstract Sleep occupies a large part of our lives and is widely believed to perform essential functions. During sleep, the neuronal rules of engagement and population dynamics are clearly different than waking. There is extensive evidence that one primary function of sleep is to consolidate memories formed during waking. Recent work, however, suggests that sleep may also actively alter neural connections to achieve forgetting (‘unlearning’). How the brain balances learning and forgetting, exactly how sleep contributes, and the ultimate effects on ensembles and behavior are unknown. The goal of this proposal is to determine how neuronal population dynamics during non-rapid-eye- movement sleep (NREMS) achieve learning vs forgetting. We specifically aim to examine how activity during NREMS regulates coupling across the cortex and the striatum, trial-to-trial variability of neural ensembles (“representational variance”) and thereby behavioral automaticity versus flexibility. Automaticity – the consistent production of a contextually-driven, fast and accurate action – is associated with increased cortico-striatal coupling and reliable ensemble activity (low variance). In contrast, weaker cortico-striatal coupling is associated with flexible, exploratory states and increased representational variance. We will use a rat model of motor skill learning (reach-to-grasp), together with multi-site electrophysiology, identification of specific neuronal subtypes, closed-loop perturbations, and detailed computational modelling. We will determine how specific NREMS dynamics differentially regulate the reactivation of neuronal ensembles, to enable distinct forms of activity-dependent plasticity. Based on extensive preliminary data, we hypothesize that global slow oscillations coupled to spindles trigger globally coordinated reactivations that are broadly coordinated between cortical and striatal subregions. These coordinated reactivations strengthen functional connections and produce earlier firing of ‘direct pathway’ striatal neurons, promoting automaticity. By contrast, local up-states occurring during delta waves produce uncoordinated reactivations, leading to functional decoupling of corticostriatal connections and earlier firing of ‘indirect pathway’ striatal neurons to promote exploration and flexibility. Our closely integrated electrophysiological, optogenetic and computational investigations will provide novel mechanistic insights into precisely how brain dynamics during sleep achieve both consolidation and unlearning of specific behavioral patterns. This knowledge has great potential to help us better understand and treat the wide range of neurological and psychiatric illnesses associated with alterations in cortico-striatal processing.