Dynamic neural coding of spectro-temporal sound features during free movement

项目摘要 尽管听觉假体技术不断进步，但听力损失的患者往往难以理解。 嘈杂环境中的语音和其他重要声音。这在一定程度上是由于退化的空间和 频谱声音信息，由听力正常的监听者利用它来解析 真实的世界。目前对空间处理的理解主要来自于研究对象是 相对于声源固定头部。尽管有这种占主导地位的实验范式，但现实世界中的听众 条件通常在空间中移动，同时定位它们的头部以提高它们的理解能力 听觉信号。前庭、运动和听觉系统之间的神经联系的存在 表明听者的动作和身体姿势为听觉系统提供了实质性的输入，以 促进听力。更好地理解健康的听觉系统是如何在通过 声学环境将为听力障碍的新治疗提供支持。 目前的研究将调查关于听者的动作和身体/头部姿势的信息如何影响 听觉皮质中的声音处理。从历史上看，对自由移动对象的研究一直受到 在复杂的声场中自由移动时，精确测量听觉输入的难度很大。 计算机、机器学习和神经记录技术的最新进展现在使这一问题 很容易驯服。有两个具体目标。第一种是同时从大量的听觉记录 在经过校准的声场中自由运动的皮质神经元。这些实验将开发出 准确跟踪声音输入所需的设备、实验方法和计算技术 在通过听觉场景的运动中传递到每一只耳朵。第二个目标将评估职位和 自我运动影响听觉皮质的声音编码。最近开发的方法使用人工神经网络 以预测复杂自然声音诱发的单个神经元的活动。这些算法将更新为 包括身体姿势和自我运动作为输入，允许测量响应属性如何 根据这些变量进行更改。通过描述自由活动动物的动态声音编码，这些 研究将为在更自然的条件下听觉系统过程的声音提供新的见解 并且可以支持用于听觉假体的改进的信号处理算法。

Project Summary Despite ongoing advances in auditory prostheses, patients with hearing loss often have difficulty understanding speech and other important sounds in noisy environments. This is due, in part, to degraded spatial and spectral sound information, which is leveraged by normal-hearing listeners to parse concurrent sounds in the real world. Current understanding of spatial processing is drawn primarily from studies in which the subject is head-fixed relative to the sound sources. Despite this dominant experimental paradigm, listeners in real-world conditions typically move through space while orienting their head to improve their ability to understand auditory signals. The existence of neural connections between the vestibular, motor, and auditory systems suggests that a listener's movement and body posture provide substantial input to the auditory system to facilitate hearing. A better understanding of how the healthy auditory system operates while moving through an acoustic environment will support new treatments for auditory disorders. The current study will investigate how information about a listener's motion and body/head posture influence sound processing in the auditory cortex. Historically, studies in free-moving subjects have been limited by the difficulty of precisely measuring auditory input during unconstrained movement through a complex sound field. Recent advances in computing, machine learning, and neural recording technology now make this problem tractable. There are two specific aims. The first is to simultaneously record from large numbers of auditory cortex neurons during free movement through a calibrated sound field. These experiments will develop the equipment, experimental approach, and computational techniques needed to accurately track the sound input to each ear during movement through an auditory scene. The second aim will evaluate how the position and self-motion impact sound coding in auditory cortex. Recently developed methods use artificial neural networks to predict the activity in single neurons evoked by complex natural sounds. These algorithms will be updated to include body posture and self-motion as inputs, allowing measurement of how response properties may change based on these variables. By characterizing dynamic sound coding in free-moving animals, these studies will provide new insight into how the auditory system processes sound under more natural conditions and can support improved signal processing algorithms for auditory prostheses.