Computational Cognitive Neuroscience of Human Auditory Cortex

人类听觉皮层的计算认知神经科学

• 批准号: 9797408
• 负责人: Josh H McDermott
• 金额: $ 33.79万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9797408
• 关键词: Address; Adopted; Auditory; Auditory Perception; Auditory area; Auditory system; Behavioral; Biological Assay; Brain; Brain region; Characteristics; Child; Cochlea; Cochlear Implants; Computer Simulation; Data; Development; Devices; Drops; Ear; Floor; Frequencies; Frustration; Functional Magnetic Resonance Imaging; Goals; Hearing Aids; Hearing problem; Human; Implant; Investigation; Lead; Location; Measurement; Measures; Methodology; Methods; Modeling; Music; Neural Network Simulation; Neurodevelopmental Disorder; Neurons; Noise; Pathway interactions; Peripheral; Persons; Pitch Perception; Population; Process; Property; Prosthesis; Psychophysics; Research; Rest; Social isolation; Sound Localization; Source; Speech; Stimulus; Structure; Surface; System; Techniques; Testing; Therapeutic Intervention; Time; Training; Visual Pathways; artificial neural network; auditory pathway; cognitive neuroscience; computational basis; deep learning; deep neural network; developmental disease; experimental study; hearing impairment; improved; insight; interest; network architecture; neural network; neuromechanism; neurophysiology; normal hearing; novel; relating to nervous system; remediation; response; sound; sound frequency; speech processing; statistics

PROJECT SUMMARY Humans with normal hearing excel at deriving information about the world from sound. Our auditory abilities represent stunning computational feats that only recently have been replicated to any extent in machine systems. And yet our auditory abilities are highly vulnerable, being greatly compromised in listeners with hearing impairment, cochlear implants, and auditory neurodevelopmental disorders, particularly in the presence of noise. Difficulties in recognition often lead to frustration and social isolation, and are not adequately addressed by current hearing aids, implants, and remediation strategies. The long-term goal of the proposed research is to reveal the basis of auditory recognition and to provide insights that will facilitate improved prosthetic devices and therapeutic interventions. The development of more effective devices and therapies is currently limited by an incomplete understanding of the factors that underlie real-world recognition by normal-hearing listeners. In particular, although responses to sound in subcortical auditory pathways are relatively well studied, little is known about the transformations that occur within the auditory cortex to create representations of meaningful sound structure. We propose to enrich the understanding of auditory recognition with three sets of experiments that examine the cortical representation of real-world sounds in human listeners, combining functional magnetic resonance imaging (fMRI) with computational modeling of the underlying representations. Aim 1 develops artificial neural network models of speech and music processing and compares their representations to those in the auditory cortex, synthesizing and then measuring brain responses to sounds that generate the same response in a model, and probing the time scale of the auditory analysis of speech and music. Aim 2 develops and tests models of pitch perception in noise, exploring the hypothesis that pitch perception is constrained both by the statistics of natural sounds and the frequency selectivity of the cochlea. Aim 3 develops and tests models that jointly localize and recognize sounds, and probes the brain representations of sound identity and location using fMRI. The results will reveal the mechanisms underlying robust sound recognition by the healthy auditory system and will set the stage for investigations of the cortical consequences of hearing impairment and auditory developmental disorders, hopefully suggesting new strategies for remediation.

项目总结 听力正常的人类擅长从声音中获取关于世界的信息。我们的听觉能力 代表了最近才在机器上复制的令人惊叹的计算壮举 系统。然而，我们的听觉能力是非常脆弱的，在听众中受到极大的损害 听力障碍、人工耳蜗植入和听神经发育障碍，特别是在 存在噪音。承认上的困难往往会导致挫折感和社会孤立，而不是 目前的助听器、植入物和补救策略充分解决了这一问题。中国的长期目标是 拟议的研究旨在揭示听觉识别的基础，并提供有助于 改进假肢装置和治疗干预措施。更有效的设备和设备的开发 目前，治疗方法受到对现实世界认知基础因素的不完全理解的限制 由听力正常的听众。特别是，尽管皮质下听觉通路对声音的反应是 相对较好的研究，对发生在听觉皮质内的变化所知甚少 有意义的声音结构的表示。我们建议丰富对听觉识别的理解 通过三组实验来检验人类听者对真实世界声音的大脑皮层表征， 结合功能磁共振成像(FMRI)和基础脑组织的计算模型 申述。目标1开发语音和音乐处理的人工神经网络模型 将它们的表达与听觉皮质中的表达进行比较，合成并测量大脑 对在模型中产生相同响应的声音的响应，并探测听觉的时间尺度 演讲和音乐的分析。目标2开发和测试噪声中的音调感知模型，探索 认为音调感知既受自然声音统计又受频率限制的假设 耳蜗的选择性。Aim 3开发和测试了联合定位和识别声音的模型，以及 使用功能磁共振成像探索声音识别和位置的大脑表征。结果将揭示 通过健康的听觉系统进行稳健的声音识别的基础机制，将为 听力障碍和听觉发育障碍的皮质后果的调查， 希望能提出新的补救策略。