Functional and computational characterization of the human auditory cortex

据NIDCD称，美国有600万至800万人拥有某种形式的言语或交流能力 disorder.语音感知要求听者将可变的声学信号映射到有限的音位集合上， 分类称为音素，并整合这些类别随着时间的推移，以形成更大的语言单位， 音节和单词。这些不同的语音特征在哪里被编码， 需要皮层计算来从声学信号计算它们。更好地了解是什么 涉及到神经回路，它们是如何组织的，以及它们执行什么计算来支持语音 理解对于开发言语感知的详细神经生物学模型是至关重要的。主要目标 该建议的一个重要目的是使用一个联合框架来研究声学和语言特征的编码， 自然语音处理的计算基础，使用侵入性表面和深度电极 植入神经外科病人体内为了研究听觉特征的皮层组织，我们将 表征听觉皮层区域中声学特征的编码和解剖组织。研究 语言特征的皮层组织，我们将测量语音，语音定位和 语义信息使用多元线性回归。为了理解底层的计算 机制，我们将训练卷积神经网络模型来预测对语音的神经反应， 使用一种新颖的方法将它们的计算表示为一组线性变换。通过解释这些模型，我们 将揭示用于不同听觉区域的非线性计算，并将其与声学编码联系起来。 和语言特征。这些互补的分析将扩展我们在语音处理方面的知识， 人类的听觉皮层，并导致新的假说的机制，各种语音和语言 紊乱总之，这项研究将大大改善目前的皮层语音处理模型， 这在包括神经语言学、言语病理学、言语假体、 和语音技术。

According to NIDCD, 6 to 8 million people in the United States have some form of speech or communication disorder. Speech perception requires a listener to map variable acoustic signals onto a finite set of phonological categories known as phonemes, and to integrate those categories over time to form larger linguistic units such as syllables and words. It remains speculative where these different speech features are encoded and what cortical computations are needed for their calculation from an acoustic signal. A better understanding of what neural circuits are involved, how they are organized, and what computations they perform to support speech comprehension is critical for developing a detailed neurobiological model of speech perception. The major aim of this proposal is to use a joint framework to study the encoding of acoustic and linguistic features and the computational underpinnings of natural speech processing, using invasive surface and depth electrodes implanted in human neurosurgical patients. To study the cortical organization of acoustic features, we will characterize the encoding and anatomical organization of acoustic features in auditory cortical regions. To study the cortical organization of linguistic features, we will measure the encoding of phonetic, phonotactic, and semantic information using multivariate linear regression. To understand the underlying computational mechanisms, we will train convolutional neural network models to predict the neural responses to speech and use a novel method to express their computation as a set of linear transforms. By interpreting these models, we will uncover nonlinear computations used in different auditory areas and relate them to the encoding of acoustic and linguistic features. These complementary analyses will extend our knowledge of speech processing in the human auditory cortex and lead to new hypotheses about the mechanisms of various speech and language disorders. Together, the proposed research will greatly improve the current models of cortical speech processing, which are of great interest in many disciplines including neurolinguistics, speech pathology, speech prostheses, and speech technologies.