Single neuron mechanisms of sensory-motor learning

描述(由申请者提供)：人类在很长一段时间内--几天、几年甚至几十年--都能保持习得的运动技能。然而，关于大脑是如何实现这种稳定性的，我们知之甚少。一些研究表明，尽管运动技能可以保持多年稳定，但控制它们的单个神经元可能会在几个小时的过程中显著改变它们的放电特性。在另一种观点中，单个神经元的调谐就像运动技能本身一样稳定。这个项目的中心假设是，大脑在两个不同的水平上编码学习到的行为--一个是高度稳定的中观水平，另一个是单一的微观水平 神经元会发生变化，并受到近期运动表现错误史的影响。换句话说，记忆的稳定性不是植根于单个神经元的稳定性，而是植根于尽管单个神经元的活动漂移而持续存在的网络模式。这个项目通过检测斑马雀鸣叫的神经基础来研究这一假说。支持歌唱行为的神经回路得到了很好的定义和广泛的研究，并且在关键方面与哺乳动物感觉-运动学习的皮质-基底节回路同源。对于这个项目来说，鸣鸟的关键价值在于其行为的稳定性。一只鸣鸟可以多年来以极高的精度唱同一首学来的歌，这为我们提供了一个独特的机会来研究运动技能是如何在很长一段时间内保持的。使用从神经元稳定记录的新工具，该项目研究了从几天到几个月的时间尺度上影响歌曲的单个神经元调谐和网络模式。为了加速歌曲运动程序的变化，该项目使用了一个脑机接口，每当大脑激活特定的神经元组时，它就会在唱歌时产生短暂的爆发噪音。初步数据显示，鸟类可以学习减少这种干扰噪音，并通过控制鸣叫的模式来提高它们的歌唱质量 靶向神经元中的活动。通过脑机接口和其他实验，重要的初步数据显示，尽管运动前皮质的介观动力学模式是稳定的，但单个神经元可以漂移到和离开集合模式，并调整它们的活动以最大限度地减少操作误差。该项目将通过细胞分辨率揭示这一过程的规律。从这些实验中获得的见解有可能影响人类健康。如果单个神经元在运动控制中漂移，那么了解管理这种漂移的规则将对促进损伤后恢复的治疗干预至关重要，或者为人类假肢创造紫貂脑机接口。

DESCRIPTION (provided by applicant): Humans maintain learned motor skills over long time-scales-for days, years or even decades. However, little is known about how the brain achieves this stability. Some studies indicate that while motor skills can remain stable for years, the individual neurons controlling them may significantly change their firing properties over the course of hours. In another view, the tuning of individual neurons is as stable as the motor skill itself. The central hypothesis of this project is that the brain encodes learned behaviors on two distinct levels - a mesoscopic level that is highly stable, and a microscopic level in which single neurons change and are influenced by the recent history of motor performance errors. In other words, the stability of a memory is rooted not in single neuron stability, but in network patterns that persist in spite of drifting activity in individual neurons. This project investigates this hypothesis by examining the neural basis of song in zebra finches. The neural circuits that underly song behavior are well defined, extensively studied, and in key respects homologous to the cortico-basal ganglia circuits that underly sensory-motor learning in mammals. For this project, the key value of the songbird is the stability of its behavior. A songbird can sing the same learned song with great precision for years providing a unique opportunity to examine how motor skills are preserved over long time-scales. Using new tools for stable recording from neurons, the project examines single neuron tuning and network patterns underlying song over time scales of days to months. To accelerate changes in the song motor program the project uses a brain-machine interface that generates brief bursts of noise during singing whenever the brain activates specific groups of neurons. Preliminary data reveals that birds can learn to reduce this interfering noise, and improve the quality of their songs by controlling the pattern of activity in the targeted neurons. Through the brain-machine interface and other experiments, significant preliminary data reveals that whereas mesoscopic dynamical patterns in premotor cortex are stable, individual neurons can drift in and out of the ensemble pattern, and adjust their activity to minimize performance errors. This project will reveal the rules of this process with cellular resolution. Insights gained from these experiments have the potential to impact human health. If single neurons drift in motor control, then knowing the rules that govern this drift will be critical to therapeutic interventions that promote recovery after injury, or create sable brain- machine interfaces for human prosthetics.