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

神经科学中一个尚未解决的关键问题是了解神经元如何执行学习。 系统然而，尽管经过多年的实验研究，我们对脑电活动模式的理解 在大脑中，以及在执行行为的肌肉中，仍然是初级的。因此，存在着一个关键的差距， 我们对运动学习的生物学和计算基础的理解。鸣禽提供了 生理上可访问的模型系统，其中研究运动记忆是如何形成的， 在成年期发育和执行。这些研究利用了鸣鸟模型的优势 为了测试一种新的运动学习理论，将神经元电活动模式的变化与 肌肉纤维的改善，在运动表现，代表技能学习。我们的长期目标是， 了解神经代码在学习过程中如何变化。拟议实验的目的是 了解鸟鸣声中的声音声学的自适应控制。我们的中心假设是， 运动学习的基础计算是大脑对精确定时的尖峰模式的搜索 利用肌肉生物力学来实现所需的行为。这个假设来自于我们之前的 这项研究表明，在已经完全学会唱歌的成年鸣禽中， 肌纤维采用基于尖峰定时的代码（而不是基于速率的代码）来控制发声行为。 这些结果与其他数据相结合，表明神经活动的统计数据随着时间的推移而发生显着变化。 鸟首先学会唱歌，表明发声技能学习的关键变化是一个发展性的过渡 从基于速率的尖峰代码到基于定时的代码。采用了一些新颖的实验和 利用我们开发的计算工具，我们将在三个具体目标中测试我们的假设。在第一个目标中， 我们将比较幼鸟在学习之前和之后运动皮层神经元所使用的尖峰信号代码。 唱歌，提供洞察大脑的运动控制神经策略是否以及如何改变， 学习在第二个目标中，我们将分析相同时间间隔内肌纤维的尖峰数据， 以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， 量化肌肉激活的变化如何在面对发育变化时改善运动表现 在生物力学中。在第三个目标中，我们将连续记录单个运动单位的尖峰活动， 成年鸟类执行快速运动学习任务，揭示了在一个单一单位内发生的变化， 学习

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.