Temporal Pattern Perception Mechanisms for Acoustic Communication

项目摘要/摘要： 处理水声通信信号是最困难但也是最重要的能力之一 听觉系统。这些能力是语言和语音处理的核心，它们的 成功或失败对人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 辅助听力设备，用于改善对学习障碍的诊断和治疗 以及沟通障碍，如听觉处理障碍、阅读障碍和特定的 语言障碍。人类非侵入性神经科学技术揭示了 语言相关的处理，但没有回答单个神经元和神经回路是如何 实现与语言相关的计算。因此，电路级的神经计算机制 对声学通信信号处理的支持仍然知之甚少。多条线路 研究表明，鸣禽可以为研究共享提供一个很好的模型 与语言相关的听觉处理能力。这项建议研究神经机制。 鸣禽和人类共有的听觉时间模式处理能力。在AIM 1，我们测试了一个强大的建模框架的细胞级别预测，该框架称为预测 编码，作为一种通用的计算机制提出，以支持学习的识别 在多个时间尺度上复杂的时间图案信号。我们结合了最先进的 具有多电极电生理学的机器学习方法，以测试显式模型 单个皮质神经元和神经中的自然刺激表示、预测和错误编码 人口。与语音整合的听觉感知的一个方面是信号的离散化 变成习得的绝对感知的声音(音素)。在目标2中，我们使用预测编码 研究自然听觉范畴的习得范畴知觉的框架 大脑皮层神经元的群体。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，为语言学习者和听者提供线索 消除感知上相似的声音的歧义。AIM2还考察了范畴神经如何 表征受时间语境的影响。除了这些音素出现在 序列，语音处理还需要知道这些元素出现在哪里。敏感度 语音序列的统计规律在婴儿学习说话之前很久就建立起来了， 并在整个成年过程中继续影响认知和理解。鸣禽也是 注意他们的声音交流信号的统计规律。在目标3中，我们将重点放在 序列特定信息是如何由单个神经元和神经细胞群体编码的 听觉皮质。拟议的办法允许在短期内取得进展，以便建立 基础语言相关能力的基本神经生物学基础和一般 可以在其中提出更复杂、更独特的人类过程的框架，以及 最终进行了测试。

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.