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

神经科学中一个关键的悬而未决的问题是理解神经质是如何实现学习的 系统。然而，尽管进行了多年的实验研究，我们对电活动模式的理解 在大脑和实施行为的肌肉中，仍然是初级的。因此，关键差距存在于 我们对运动学习的生物学和计算基础的理解。鸣禽提供了一种 生理上可访问的模型系统，在其中研究运动记忆是如何形成的 发展并在成年后被处死。拟议中的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，将神经元和神经元电活动模式的变化联系起来 肌肉纤维有助于运动能力的提高，这是技能学习的典型特征。我们的长期目标是充分 了解神经编码在学习过程中的变化。拟议的实验的目的是 了解鸟鸣中发声声学的自适应控制。我们的中心假设是 运动学习的基本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达到预期的行为。这一假设是从我们以前的 研究表明，在成年鸣鸟中，它们已经完全学会了它们的歌声，包括神经元和发声 肌肉纤维使用基于脉冲计时的代码(而不是基于速率的代码)来控制发声行为。 这些结果，结合其他数据显示，神经活动的统计数据随着 伯德首先学会唱歌，这表明声乐技能学习的关键变化是一个发展的转变 从基于速率的尖峰代码到基于定时的代码。采用了许多新颖的实验和 利用我们开发的计算工具，我们将在三个具体目标上测试我们的假设。第一个目标是， 我们将比较幼鸟学习前后运动皮质神经元使用的尖峰代码。 提供了关于大脑运动控制的神经策略是否以及如何在 学习。在第二个目标中，我们将分析同一时间间隔内肌肉纤维的峰值数据，从而使我们能够 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体外方法， 量化肌肉激活的变化如何在面对发育变化时改善运动能力 在生物力学方面。在第三个目标中，我们将持续记录单个马达单位的尖峰活动，同时 成年鸟类执行快速运动学习任务，揭示在一个单位内发生的变化 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.