Functional and computational characterization of the human auditory cortex

根据NIDCD的数据，美国有600万到800万人有某种形式的语言或交流 无序。语音感知要求听者将不同的声学信号映射到有限的语音集合上 被称为音素的类别，并随着时间的推移整合这些类别以形成更大的语言单位，如 作为音节和单词。这些不同的语音特征在哪里编码以及在什么地方编码仍然是推测的 根据声学信号进行计算需要大脑皮层计算。更好地理解 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 理解对于建立一个详细的言语感知神经生物学模型至关重要。主要目标是 这项建议的目的是使用一个联合框架来研究声学和语言特征的编码以及 使用有创表面电极和深度电极的自然语音处理的计算基础 植入人类神经外科患者体内。为了研究声学特征的大脑皮层组织，我们将 描述听觉皮质区域的声学特征的编码和解剖组织。学习 语言特征的大脑皮层组织，我们将测量语音、音位和 语义信息采用多元线性回归。要了解基本的计算 机制，我们将训练卷积神经网络模型来预测对语音和 使用一种新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 将揭示在不同听觉区域使用的非线性计算，并将它们与声学编码相关联 和语言特点。这些互补的分析将扩展我们对语音处理的知识 人类听觉皮质，并导致了关于各种言语和语言机制的新假说 精神错乱。总之，拟议的研究将极大地改进目前的皮质语音处理模型， 它对许多学科都很感兴趣，包括神经语言学，语音病理学，语音假体， 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.