Single neuron mechanisms of sensory-motor learning

感觉运动学习的单神经元机制

• 批准号: 9509566
• 负责人: Timothy James Gardner
• 金额: $ 29.27万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2019-12-03
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9509566
• 关键词: Accelerometer; Address; Adult; Animal Behavior; Area; Basal Ganglia; Behavior; Birds; Brain; Cells; Code; Data; Disease; Fire - disasters; Future; Health; Hour; Human; Individual; Injury; Learning; Length; Mammals; Martes zibellina; Measurement; Measures; Memory; Microscopic; Motor; Motor Cortex; Motor Skills; Movement; Nature; Neurons; Noise; Patients; Pattern; Performance; Plant Roots; Process; Property; Prosthesis; Published Comment; Recording of previous events; Recovery; Research; Resolution; Rewards; Sensory; Signal Transduction; Songbirds; Testing; Therapeutic; Therapeutic Intervention; Time; Trauma; User-Computer Interface; bird song; brain computer interface; brain machine interface; calcium indicator; design; experimental study; improved; insight; learned behavior; millisecond; motor control; motor learning; nerve injury; neural circuit; programs; public health relevance; rate of change; relating to nervous system; sensory feedback; sensory mechanism; spatiotemporal; tool; zebra finch

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.

描述（由申请人提供）：人类在很长的时间尺度上保持学到的运动技能-几天，几年甚至几十年。然而，人们对大脑如何实现这种稳定性知之甚少。一些研究表明，虽然运动技能可以保持稳定多年，但控制它们的单个神经元可能会在几个小时内显著改变其放电特性。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，大脑在两个不同的层面上编码学习行为-一个是高度稳定的中观层面，一个是微观层面，其中单个 神经元发生变化，并受到近期运动表现错误的影响。换句话说，记忆的稳定性并不取决于单个神经元的稳定性，而是取决于网络模式，尽管单个神经元的活动在漂移，但网络模式仍然存在。本计画借由研究斑胸草雀鸣唱的神经基础来探讨这个假说。歌唱行为背后的神经回路已经被很好地定义和广泛研究，并且在关键方面与哺乳动物感觉运动学习背后的皮质基底神经节回路相似。对于这个项目来说，鸣禽的关键价值在于其行为的稳定性。一只鸣禽可以多年来非常精确地唱同一首学过的歌，这提供了一个独特的机会来研究运动技能是如何在长时间尺度上保存下来的。该项目使用新的神经元稳定记录工具，研究了在几天到几个月的时间尺度上，单个神经元的调谐和歌曲背后的网络模式。为了加速歌曲运动程序的变化，该项目使用了一种脑机接口，每当大脑激活特定的神经元组时，这种接口就会在唱歌过程中产生短暂的噪音。初步的数据显示，鸟类可以学会减少这种干扰噪音，并通过控制声音的模式来改善它们的歌声质量。 目标神经元的活动。通过脑机接口和其他实验，重要的初步数据表明，虽然运动前皮层的介观动力学模式是稳定的，但单个神经元可以漂移进入和退出整体模式，并调整其活动以最大限度地减少性能错误。该项目将揭示这一过程的细胞分辨率的规则。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发生漂移，那么了解控制这种漂移的规则对于促进损伤后恢复的治疗干预或为人类假肢创造稳定的脑机接口至关重要。