Temporal Pattern Perception Mechanisms for Acoustic Communication

声音交流的时间模式感知机制

• 批准号: 9803507
• 负责人: TIMOTHY Q GENTNER
• 金额: $ 33.46万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9803507
• 关键词: Acoustics; Adult; Affect; Algorithms; Animal Model; Auditory; Auditory Perception; Auditory Perceptual Disorders; Auditory area; Auditory system; Behavior; Behavioral; Biological Models; Categories; Code; Cognition Disorders; Cognitive; Communication; Communication impairment; Complex; Comprehension; Computer Simulation; Cues; Data; Detection; Diagnosis; Disease; Dyslexia; Electrodes; Electrophysiology (science); Elements; Failure; Foundations; Generations; Goals; Grouping; Hearing Aids; Heart; Human; Individual; Infant; Language; Language Development; Learning; Learning Disabilities; Longevity; Machine Learning; Modality; Modeling; Neurobiology; Neurons; Neurosciences; Parietal; Pattern; Perception; Physiological; Plant Roots; Population; Process; Quality of life; Research; Sensory; Signal Transduction; Songbirds; Speech; Speech Perception; Speech Sound; Stimulus; Stream; Sturnus vulgaris; Superior temporal gyrus; Techniques; Testing; Time; Work; auditory processing; autism spectrum disorder; bird song; cognitive process; computer framework; experience; experimental study; hearing impairment; improved; language comprehension; language processing; learned behavior; learning strategy; model development; neural circuit; neurobiological mechanism; neuromechanism; pattern perception; relating to nervous system; response; sensory input; sensory system; signal processing; sound; spatiotemporal; specific language impairment; speech processing; speech recognition; statistics; success

Project Summary/Abstract: Processing acoustic communication signals is among the most difficult yet vital abilities of the auditory system. These abilities lie at the heart of language and speech processing, and their success or failure has profound impacts on quality of life across the lifespan. Understanding the neurobiological mechanisms that support these basic abilities holds promise for advancing assistive listening devices, and for improving diagnoses and treatments for learning disabilities and communication disorders, such as auditory processing disorder, dyslexia, and specific language impairment. Non-invasive neuroscience techniques in humans reveal the loci of language-related processing but do not answer how individual neurons and neural circuits implement language-relevant computations. Thus, circuit-level neuro-computational mechanisms that support acoustic communication signal processing remain poorly understood. Multiple lines of research suggest that songbirds can provide an excellent model for investigating shared auditory processing abilities relevant to language. This proposal investigates neural mechanisms of auditory temporal pattern processing abilities shared between songbirds and humans. In Aim 1, we test the cellular-level predictions of a powerful modelling framework, called predictive coding, proposed as a general computational mechanism to support the learned recognition of complex temporally patterned signals at multiple timescales. We combine state-of-the-art machine learning methods with multi-electrode electrophysiology, to test explicit models for natural stimulus representation, prediction, and error coding in single cortical neurons and neural populations. One aspect of auditory perception integral to speech is the discretization of the signal into learned categorically perceived sounds (phonemes). In Aim 2, we use the predictive coding framework to investigate the learned categorical perception of natural auditory categories in populations of cortical neurons. In humans, the transition statistics between adjacent phonemes can aid or alter phoneme categorization, providing cues for language learners and listeners to disambiguate perceptually similar sounds. Aim2 also examines how categorical neural representations are affected by temporal context. In addition to which phonemes occur in a sequence, speech processing also requires knowing where those elements occur. Sensitivities to the statistical regularities of speech sequences are established long before infants learn to speak, and continue to affect both recognition and comprehension throughout adulthood. Songbirds also attend to the statistical regularities in their vocal communication signals. In Aim 3, we focus on how sequence-specific information is encoded by single neurons and neural populations in auditory cortex. The proposed approach permits progress in the near term towards establishing the basic neurobiological substrates of foundational language-relevant abilities and a general framework within which more complex, uniquely human processes, can be proposed and eventually tested.

项目摘要/摘要： 处理声通信信号是最困难但至关重要的能力之一 听觉系统。这些能力是语言和语音处理的核心，他们 成功或失败对整个生命周期的生活质量产生了深远的影响。了解 支持这些基本能力的神经生物学机制有望前进 辅助聆听设备，以及改善学习障碍的诊断和治疗方法 和沟通障碍，例如听觉处理障碍，阅读障碍和特定 语言障碍。人类的非侵入性神经科学技术揭示了 与语言有关的处理，但没有回答单个神经元和神经电路的方式 实施与语言相关的计算。因此，电路级神经计算机制 支持声通信信号处理的理解仍然很少。多行 研究表明，鸣禽可以提供一个出色的模型来调查共享 听觉处理能力与语言有关。该建议调查神经机制 鸣禽和人类之间共享的听觉时间模式处理能力。目标 1，我们测试强大的建模框架的蜂窝级预测，称为预测 编码，作为一种一般计算机制，以支持对 在多个时间尺度上具有复杂的暂时性信号。我们结合了最新的 具有多电极电生理学的机器学习方法，以测试明确的模型 自然刺激表示，预测和单个皮质神经元和神经中的错误编码 人群。语音不可或缺的听觉感知的一个方面是信号的离散化 进入学到的被绝对感知的声音（音素）。在AIM 2中，我们使用预测性编码 框架研究自然听觉类别的学习分类感知 皮质神经元的种群。在人类中，相邻音素之间的过渡统计数据 可以帮助或改变音素分类，为语言学习者和听众提供线索 在感知上消除歧义的声音。 AIM2还检查了分类神经 表示形式受时间上下文的影响。除了音素出现在 序列，语音处理还需要知道这些元素的发生位置。对 语音序列的统计规律是在婴儿学会说话之前建立的 并继续影响整个成年期的认识和理解。鸣禽也是 在声音传播信号中遵守统计规律。在AIM 3中，我们专注于 序列特异性信息如何由单个神经元和神经种群编码 听觉皮层。拟议的方法允许在建立的近期进展 基础语言与基础语言的基本基础和一般性的基础 可以提出更复杂，更独特的人类过程的框架 最终进行了测试。