Neuromechanics of learned sensorimotor vocal integration

描述(由申请人提供)：大脑皮质如何连续、快速地产生声音，以及大脑皮质区域的声音如何变化，目前仍知之甚少。这种知识上的差距使言语和语言产生的适当皮质模型的发展复杂化，该模型可以帮助解释言语和语言的病理。本文从生物力学的角度出发，建立了斑雀空洞和上声道的数学动力学系统模型。该模型成功地在描述复杂的歌唱行为方面引入了简化，识别出组成鸟类歌声的一系列基本“手势”。手势是鸟儿歌唱时，随着时间的推移，压力(改变通过空洞的气量)和张力(空洞膜)的协调变化的微小发声动作。研究大脑皮质歌唱系统区域压力和张力随时间变化特征的神经关联，发现HVC神经元的活动编码运动中的重要时刻(手势轨迹中的极端或最大点)。这表明HVC有一个发声行为的内部模型，由手势序列表示。神经元活动的时间恰好与鸟类歌唱神经元编码的手势的时间几乎为零的时间滞后有关。由于神经元活动和歌唱之间应该有很大的传导延迟，这就提出了HVC中的活动是对运动输出的预测的假设。这提出了一种新的发声运动控制组织模型：HVC投射神经元群体的输出代表了手势动力学的预测(前向模型)，用于处理反馈，而向运动前活动的转换发生在初级运动皮质RA。有两个具体目标将检验这些假说。第一个目标是利用睡眠鸟类和鸣禽的单个已识别的HVC神经元的录音来测试 手势假说的预测。反馈将受到干扰，以评估它是否影响被假设为预测的机械基础的非线性时间总和。数据将根据对手势轨迹参数敏感的统计模型进行评估。第二个目标将检验这样的假设，即从HVC到RA的信息变化涉及从手势极端表征到通过歌曲的连续路径的转换。RA的地形组织将通过RA的系统记录进行评估。在一个相关的目标中，将通过引入双边互动和依赖时间的上声道过滤来改进该模型，并创建自动化程序来促进歌曲的快速分析 对其他目标的刺激。这些研究可以对发声运动编码产生新的见解，并为人类语音产生的研究提供信息。

DESCRIPTION (provided by applicant): How sequential, rapid vocal production is represented in the cortex, and how representations change across cortical regions remains poorly understood. This gap in knowledge complicates development of an adequate cortical model for speech and language production which could help to explain speech and language pathologies. Here, a biomechanical perspective is taken to develop a mathematical dynamical systems model of the zebra finch syrinx and upper vocal tract. The model successfully introduces simplifications in describing the complex singing behavior, identifying a sequence of elemental "gestures" that comprise the bird's song. Gestures are small vocal movements, coordinated changes in pressure (varying the amount of air going through the syrinx) and changes in tension (of the syringeal membrane) as a function of time as a bird sings. Examining neural correlates of the time-varying features of pressure and tension in a cortical song system area HVC has identified that the activity of HVC neurons encode significant moments during movements (extreme or maximal points in the gesture trajectories). This indicates that HVC has an internal model of vocal behavior, represented by sequences of gestures. The timing of neuronal activity is precisely associated with near zero time lag to the time a bird is singing the gesture encoded by the neurons. Since there should be a substantial conduction delay between neuronal activity and singing, this motivates the hypothesis that the activity in HVC is a prediction of motor output. This suggests a new model of organization of vocal motor control: the output of the population of HVC projection neurons represents a predication ("forward" model) of the dynamics of gestures, used to process feedback, and the conversion to pre-motor activity occurs in primary motor cortex RA. Two specific aims will test these hypotheses. The first aim will use recordings of single identified HVC neurons in sleeping birds and in singing birds to test predictions of the gesture hypothesis. Feedback will be perturbed to assess if it affects non-linear temporal summation hypothesized to be the mechanistic basis for the prediction. The data will be assessed in terms of a statistical model sensitive to parameters of gesture trajectories. The second aim will test the hypothesis that the change of information from HVC to RA involves transformations from representations of gesture extrema to continuous paths through song. The topographic organization of RA will be assessed by systematic recordings in RA. In a related aim, the model will be improved through introducing bilateral interactions and time-depending upper vocal tract filtering, and creating automated procedures to facilitate rapid analysis of song stimuli for the other aims. These studies can generate new insight into vocal motor coding, and inform studies of human speech production.