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

点击翻译按钮获取中文摘要

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.