Single neuron mechanisms of sensory-motor learning

感觉运动学习的单神经元机制

基本信息

批准号：
8927703
负责人：
Timothy James Gardner
金额：
$ 35.81万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-15 至 2019-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8927703
关键词：
Address Adult Animal Behavior Area Basal Ganglia Behavior Birds Brain Cells Code Data Disease Fire - disasters Future Health Hour Human Individual Injury Learning Length Life Mammals Martes zibellina Measurement Measures Memory Microscopic Monitor Motor Motor Cortex Motor Skills Movement Nature Neurons Noise Patients Pattern Performance Plant Roots Process Property Prosthesis Published Comment Recording of previous events Recovery Relative (related person)Research Resolution Rewards Sensory Shapes Signal Transduction Songbirds Testing Therapeutic Therapeutic Intervention Time Trauma base bird song brain computer interface brain machine interface calcium indicator design improved insight learned behavior millisecond motor control motor learning motor skill learning nerve injury neural circuit programs relating to nervous system research study sensory feedback sensory mechanism tool zebra finch

项目摘要

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）：人类在长时间，数年甚至数十年的时间内保持学习的运动技能。但是，关于大脑如何实现这种稳定性知之甚少。一些研究表明，尽管运动技能可以保持稳定数年，但控制它们的单个神经元可能会在数小时内显着改变其射击特性。从另一种角度来看，单个神经元的调整与运动技能本身一样稳定。该项目的核心假设是大脑在两个不同的层面上编码学习的行为 - 高度稳定的介质水平，以及一个微观水平，其中单个级别神经元会发生变化，并受到运动性能错误的近期历史的影响。换句话说，记忆的稳定性不是根源于单个神经元稳定性，而是基于网络模式，这些模式仍然存在于单个神经元中的活动中的漂移。该项目通过检查斑马雀科中歌曲的神经基础来研究这一假设。基本的歌曲行为的神经回路经过了充分的定义，广泛的研究，并且在关键方面与哺乳动物中基础的感觉运动学习的皮质 - 巴萨神经节电路同源。对于这个项目，鸣禽的关键价值是其行为的稳定性。多年来，鸣禽可以精确地唱着同一首学习的歌曲，为长时间尺度提供了一个独特的机会来研究运动技能。该项目使用神经元稳定录制的新工具检查了单个神经元调整和网络模式，这些歌曲的基础是天数到几个月的时间尺度。为了加速歌曲运动程序的变化，该项目使用脑机界面，每当大脑激活特定的神经元组时，在唱歌过程中会产生短暂的噪音。初步数据表明，鸟类可以学会减少这种干扰的噪音，并通过控制模式来提高歌曲的质量靶向神经元的活性。通过脑机界面和其他实验，明显的初步数据表明，尽管介观介质的动力学模式是稳定的，但单个神经元可以从进出的集合模式中漂移，并调整其活性以最大程度地减少性能误差。该项目将通过蜂窝分辨率揭示此过程的规则。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漂移，那么知道控制这种漂移的规则对于促进受伤后康复的治疗干预措施至关重要，或为人类假体创建可黑脑脑机界面。