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: 10246259
• 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/10246259
• 关键词: Address; Adopted; Auditory; Auditory Perception; Auditory area; Auditory system; Behavioral; Biological Assay; Brain; Brain region; Characteristics; Child; Cochlea; Cochlear Implants; Computer Models; Data; Development; Devices; Drops; Ear; Floor; Frequencies; Frustration; Functional Magnetic Resonance Imaging; Goals; Hearing Aids; Hearing Tests; 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.

项目概要 听力正常的人类擅长从声音中获取有关世界的信息。我们的听觉能力 代表了令人惊叹的计算壮举，直到最近才在机器中得到一定程度的复制 系统。然而，我们的听觉能力非常脆弱，听者的听觉能力会受到很大影响。 听力障碍、人工耳蜗和听觉神经发育障碍，特别是在 是否存在噪音。获得认可的困难常常会导致沮丧和社会孤立，但事实并非如此。 当前的助听器、植入物和补救策略可以充分解决这一问题。该组织的长期目标 拟议的研究旨在揭示听觉识别的基础并提供有助于促进听觉识别的见解 改进的假肢装置和治疗干预措施。开发更有效的设备和 目前，由于对现实世界识别背后的因素的不完全理解，治疗受到限制 听力正常的听众。特别是，尽管皮层下听觉通路对声音的反应是 研究相对充分，但对于听觉皮层内发生的转变以创造 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 通过三组实验来检查人类听众对现实世界声音的皮质表征， 将功能磁共振成像 (fMRI) 与基础计算模型相结合 交涉。目标 1 开发语音和音乐处理的人工神经网络模型 将它们的表征与听觉皮层中的表征进行比较，综合然后测量大脑 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 言语和音乐的分析。 Aim 2 开发并测试噪声中的音调感知模型，探索 假设音调感知受到自然声音统计和频率的限制 耳蜗的选择性。 Aim 3 开发并测试联合定位和识别声音的模型，以及 使用功能磁共振成像探测声音识别和位置的大脑表征。结果将揭示 健康听觉系统强大的声音识别背后的机制，将为 研究听力障碍和听觉发育障碍的皮质后果， 希望提出新的补救策略。