Temporal Pattern Perception Mechanisms for Acoustic Communication

声音交流的时间模式感知机制

基本信息

批准号：
10624335
负责人：
TIMOTHY Q GENTNER
金额：
$ 33.58万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10624335
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 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 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 categorization auditory processing autism spectrum disorder bird song cognitive process computer framework experience experimental study hearing impairment improved language comprehension language processing machine learning method model development neural neural circuit neurobiological mechanism neuromechanism neuronal circuitry pattern perception response sensory input sensory system signal processing sound spatiotemporal specific language impairment speech processing speech recognition statistics success

项目摘要

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：处理声音通信信号是人类最困难但最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，它们的能力成功或失败对一生的生活质量有着深远的影响。了解支持这些基本能力的神经生物学机制有望推动进步辅助听力设备，以及改善学习障碍的诊断和治疗和沟通障碍，例如听觉处理障碍、阅读障碍和特定语言障碍。人类非侵入性神经科学技术揭示了与语言相关的处理，但不回答单个神经元和神经回路如何实现与语言相关的计算。因此，电路级神经计算机制支持声学通信信号处理的技术仍然知之甚少。多条线路的研究表明鸣禽可以为研究共享资源提供一个极好的模型与语言相关的听觉处理能力。该提案研究了神经机制鸣禽和人类共享的听觉时间模式处理能力。瞄准 1、我们测试一个强大的建模框架的细胞水平预测，称为预测编码，被提议作为一种通用计算机制来支持学习识别多个时间尺度的复杂时间模式信号。我们结合最先进的技术具有多电极电生理学的机器学习方法，用于测试显式模型单个皮层神经元和神经元中的自然刺激表示、预测和错误编码人口。语音的听觉感知的一个方面是信号的离散化转化为学习到的明确感知的声音（音素）。在目标 2 中，我们使用预测编码研究自然听觉类别的学习分类感知的框架皮质神经元群体。在人类中，相邻音素之间的转换统计可以帮助或改变音素分类，为语言学习者和听众提供线索消除感知上相似的声音的歧义。 Aim2 还研究了分类神经网络如何表征受到时间背景的影响。除了出现在音素中的序列，语音处理还需要知道这些元素出现的位置。敏感度语音序列的统计规律早在婴儿学会说话之前就已建立，并持续影响整个成年期的认知和理解。鸣鸟也注意他们声音交流信号的统计规律。在目标 3 中，我们重点关注序列特异性信息如何由单个神经元和神经群体编码听觉皮层。拟议的方法可以在短期内取得进展，以建立基础语言相关能力和一般能力的基本神经生物学基础可以在其中提出更复杂、独特的人类流程的框架最终经过测试。