Neural Basis of Song Syntax in Songbird

动作顺序是许多动物和人类行为的基础，包括运动、弹钢琴、语言和逻辑推理。顺序行为通常遵循一些动作语法，它定义了如何将基本动作串在一起形成复杂的序列，类似于在句子结构中遵循语法规则。动作句法是如何在神经系统中产生和控制的，人们知之甚少。孟加拉雀是研究这个问题的理想动物模型，因为孟加拉雀的歌声由几个音节组成，这些音节排列成具有复杂句法结构的序列。这个项目将测试一种假设，即运动前脑区HVC编码孟加拉雀鸣叫的句法结构。据推测，沿HVC中链状网络结构的尖峰神经活动的传播驱动了鸟类的鸣叫并产生了鸣叫语法。该方法结合了计算建模和孟加拉雀鸣叫控制系统中神经活动的单单元记录。神经记录将与改变声音反馈和歌曲控制系统的瞬态电刺激相结合，以阐明HVC在歌曲语法中的作用。本研究结果将对理解运动动作序列产生的神经机制产生重大影响，也可能揭示人类语音合成的神经机制。该研究汇集了来自物理学、计算神经科学和电生理学的跨学科专业知识，并涉及广泛的现代实验和计算技术。因此，它将为博士后研究人员、研究生和本科生以及暑期高中实习生提供充足的机会，以获得电生理学、神经数据分析和计算神经科学的现代方法方面的专业知识。

Sequences of actions are fundamental to many animal and human behaviors, including locomotion, playing piano, language, and logical reasoning. Sequential behaviors often follow some action syntax, which defines how elementary actions can be strung together to form complex sequences, similar to following grammatical rules in the construction of sentences. How action syntax is generated and controlled in the nervous system is poorly understood. The Bengalese finch is an ideal animal model for studying this problem, because the songs of the Bengalese finch consist of several syllables arranged into sequences with complex syntactical structures. This project will test the hypothesis that the premotor brain area HVC encodes the syntactic structure of the Bengalese finch's song. It is hypothesized that the propagation of spiking neural activity along a chain-like network structure in the HVC is driving the song of the bird and generating the song syntax. The approach combines computational modeling and single unit recordings of neural activity in the song control system of Bengalese finches. Neural recordings will be combined with altering acoustic feedback and transient electrical stimulations of the song control system to elucidate the role of the HVC in song syntax. The results of this research will have a significant impact on understanding the neural mechanisms underlying the generation of sequences of motor actions, and may also shed light on the neural mechanisms of human speech synthesis. The research brings together interdisciplinary expertise drawn from physics, computational neuroscience, and electrophysiology, and involves a wide range of modern experimental and computational techniques. Consequently, it will provide ample opportunity for postdoctoral researchers, graduate and undergraduate students, and summer high school interns to gain expertise in electrophysiology, neural data analysis and modern methods in computational neuroscience.