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

项目摘要 尽管听觉假体不断进步，但听力损失患者通常难以理解 语音和其他重要的声音在嘈杂的环境。这部分是由于退化的空间和 频谱声音信息，这是利用正常听力听众解析并发的声音在 真实的世界。目前对空间处理的理解主要来自于这样的研究， 头部相对于声源固定。尽管有这种占主导地位的实验范式， 条件通常在空间中移动，同时调整头部方向以提高理解能力 听觉信号前庭、运动和听觉系统之间存在神经联系 表明，听众的运动和身体姿势提供了大量的输入到听觉系统， 方便聆听。更好地了解健康的听觉系统如何运作， 声学环境将支持听觉障碍的新疗法。 目前的研究将探讨如何信息的听众的运动和身体/头部姿势的影响 听觉皮层的声音处理从历史上看，对自由活动受试者的研究受到了 在通过复杂声场的无约束运动期间精确测量听觉输入的困难。 计算、机器学习和神经记录技术的最新进展现在使这个问题 听话有两个具体目标。第一种是同时记录大量的听觉 皮层神经元在通过校准声场的自由运动期间。这些实验将开发出 精确跟踪声音输入所需的设备、实验方法和计算技术 在听觉场景中移动的过程中传递到每只耳朵。第二个目标将评估如何立场和 自我运动影响听觉皮层的声音编码。最近开发的方法使用人工神经网络 来预测由复杂的自然声音引起的单个神经元的活动。这些算法将被更新， 包括身体姿势和自运动作为输入，允许测量响应特性如何可以 基于这些变量的变化。通过描述自由移动动物的动态声音编码， 这些研究将为了解听觉系统在更自然的条件下如何处理声音提供新的见解 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.