Vocal motor control and sensorimotor learning - behavior, muscles, and neurons

声音运动控制和感觉运动学习 - 行为、肌肉和神经元

• 批准号: 10433831
• 负责人: Samuel Sober
• 金额: $ 34.5万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10433831
• 关键词: Acoustics; Address; Adolescent; Adult; Animal Model; Award; Back; Behavior; Behavior Control; Biological; Biological Models; Biomechanics; Birds; Brain; Code; Complex; Data; Development; Developmental Process; Electrodes; Future; Goals; In Vitro; Individual; Learning; Learning Skill; Link; Memory; Modeling; Motor; Motor Cortex; Motor Neurons; Motor output; Muscle; Muscle Fibers; Nervous System control; Nervous system structure; Neurologic; Neuronal Plasticity; Neurons; Neurosciences; Organ; Outcome; Patients; Pattern; Performance; Physiological; Play; Preparation; Research; Songbirds; System; Testing; Variant; Vocabulary; Work; analytical method; base; bird song; body mechanics; computerized tools; experimental study; fascinate; improved; in vivo; innovation; insight; learned behavior; millisecond; motor control; motor learning; nervous system disorder; neural circuit; neuromechanism; neuromuscular; novel; programs; rehabilitation service; relating to nervous system; skill acquisition; skills; statistics; theories; vocal control; vocal learning

A critical unsolved problem in neuroscience is to understand how learning is implemented by the nervous system. However, despite years of experimental study, our understanding of how patterns of electrical activity in the brain, and in the muscles that implement behavior, is still rudimentary. A critical gap therefore exists in our understanding of the biological and computational underpinnings of motor learning. Songbirds provide a physiologically accessible model system in which to investigate how motor memories are formed during development and executed in adulthood. The proposed studies exploit the strength of the songbird model to test a novel theory of motor learning, linking changes in the patterns of electrical activity in neurons and muscle fibers to the improvements in motor performance that typify skill learning. Our long-term goal is to fully understand how neural codes change during learning. The objective of the proposed experiments is to understand the adaptive control of a vocal acoustics in birdsong. Our central hypothesis is that that the fundamental computation underlying motor learning is the brain's search for the precisely-timed spike patterns that exploit muscle biomechanics to achieve the desired behavior. This hypothesis emerges from our previous work demonstrating that in adult songbirds, which have fully learned their songs, both neurons and vocal muscle fibers employ a spike-timing based code (rather than a rate-based code) to control vocal behavior. These results, combined with other data showing that the statistics of neural activity change dramatically as a bird first learns to sing, suggest that the key change underlying vocal skill learning is a developmental transition from a rate-based spike code to a timing-based code. Employing a number of novel experimental and computational tools that we have developed, we will test our hypothesis in three specific aims. In the first aim, we will compare the spiking code employed by neurons in the motor cortex before and after young birds learn to sing, providing insight into whether and how the brain's neural strategies for motor control change during learning. In the second aim, we will analyze spiking data from muscle fibers over the same interval, allowing us to determine how muscle codes change with learning, and, using innovative in vitro and ex vivo approaches, quantify how changes in muscle activation improve motor performance in the face of developmental changes in biomechanics. In the third aim, we will record spiking activity from individual motor units continuously while adult birds perform a rapid motor learning task, revealing the changes that occur within a single unit during learning.

神经科学中一个关键的未解决问题是了解神经如何实施学习 系统。但是，尽管经过多年的实验研究，我们对电活动模式的理解 在大脑和实施行为的肌肉中，仍然是基本的。因此，存在一个关键的差距 我们对运动学习的生物学和计算基础的理解。鸣禽提供 在生理上可访问的模型系统，其中可以研究如何形成运动记忆 发展并成年。提出的研究利用了鸣禽模型的强度 为了测试一种新的运动学习理论，将神经元中电活动模式的变化与 肌肉纤维可改善运动性能的运动性能。我们的长期目标是完全 了解学习过程中神经代码的变化。提出的实验的目的是 了解Birdsong声乐声学的自适应控制。我们的核心假设是 运动学习的基本计算是大脑对精确时定的尖峰模式的搜索 这种利用肌肉生物力学以实现所需的行为。这个假设来自我们以前的 作品证明了在成年鸣禽中，他们已经充分学习了他们的歌曲，无论是神经元还是声乐 肌肉纤维采用基于尖峰的代码（而不是基于费率的代码）来控制声音行为。 这些结果与其他数据相结合，表明神经活动的统计数据急剧变化为 伯德首先学会唱歌，暗示声音技能学习的关键变化是发展性过渡 从基于费率的尖峰代码到基于计时的代码。采用许多新颖的实验和 我们开发的计算工具将以三个特定目的测试我们的假设。在第一个目标中 我们将比较在幼鸟学习之前和之后，神经元在运动皮层中使用的尖峰代码 唱歌，提供有关大脑在运动过程中是否以及如何改变大脑神经策略的见解 学习。在第二个目标中，我们将在相同的间隔内分析来自肌肉纤维的尖峰数据，从而使我们 为了确定肌肉代码如何随着学习而变化，并使用创新的体外和离体方法来确定 量化面对发育变化的肌肉激活的变化如何改善运动性能 在生物力学中。在第三个目标中，我们将连续记录各个电动机单元的尖峰活动 成年鸟执行快速运动的任务，揭示了单个单元内发生的变化 学习。