Revealing the organization and functional significance of neural timescales inauditory cortex

项目摘要 人们非常善于通过声音来理解世界：在嘈杂的环境中理解语言， 餐厅，挑选出一个家庭成员的声音，或认识到一个熟悉的旋律。虽然我们把这些 能力是理所当然的，它们反映了生物工程令人印象深刻的计算壮举， 难以在机器系统中复制。我的研究计划的长期目标是开发计算 和实验方法来逆向工程大脑如何编码自然的声音，如语音， 这些进步有助于理解和帮助治疗听力障碍。的核心挑战之一 对自然声音进行编码的一个重要原因是，它们是以从毫秒到秒的许多不同时间尺度结构化的， 甚至几分钟大脑如何整合这些不同的时间尺度，从声音中获得意义？ 解决这个问题一直是一个挑战，因为没有通用的方法来衡量 大脑中的神经时间尺度。因此，我们对神经时间尺度的了解相对较少 以及这种组织如何使自然声音编码。克服这些 局限性，我们开发了一个简单的实验范式（“时间上下文不变性”或TCI范式）， 估计任何感觉反应的时间整合期：刺激改变的时间窗口 回应。我们将TCI方法应用于人类皮层电图（ECoG）和动物生理学 记录，以揭示在区域和单细胞水平上的神经时间尺度的组织（目的I）。试点 从我们的分析数据显示，时间尺度是有层次的组织，与高阶区域显示， 更长的整合期。为了探索这个时间尺度层次结构的功能意义，我们 将TCI与非常适合表征自然声音的计算技术相结合（Aim II）。我们测试是否 增加的整合周期能够实现语音的更抗噪声的表示（Aim IIA），无论区域 具有较长积分周期的编码自然声音的高阶特性（目标IIB和IIC）， 为重要的声音类别，如语音或音乐（Aim IID），以及是否 皮质整合期可以通过它们响应的特征的持续时间来解释（Aim IIE）。在 在进行这项研究的过程中，我将在两个关键领域接受培训：（1）ECoG，这是唯一的方法 通过空间和时间的精确度来理解神经时间尺度在人类大脑中是如何组织的 (2)深度神经网络（DNN）是唯一能够执行具有挑战性的感知任务的模型， 人类水平，并预测高级皮层区域的神经反应。完成培训后，我将 我有一套独特的实验（功能磁共振成像，ECoG，心理物理学）和计算技能（数据驱动的统计 建模和假设驱动的DNN建模），这将有助于我过渡到独立调查员。

Project Summary People are remarkably adept at making sense of the world through sound: understanding speech in a noisy restaurant, picking out the voice of a family member, or recognizing a familiar melody. Although we take these abilities for granted, they reflect impressive computational feats of biological engineering that are remarkably difficult to replicate in machine systems. The long-term goal of my research program is to develop computational and experimental methods to reverse-engineer how the brain codes natural sounds like speech and to exploit these advances to understand and aid in the treatment of hearing impairment. One of the central challenges of coding natural sounds is that they are structured at many different timescales from milliseconds to seconds and even minutes. How does the brain integrate across these diverse timescales to derive meaning from sound? Answering this question has been challenging because there are no general-purpose methods for measuring neural timescales in the brain. As a consequence, we know relatively little about how neural timescales are organized in auditory cortex and how this organization enables the coding of natural sounds. To overcome these limitations, we develop a simple experimental paradigm (the “temporal context invariance” or TCI paradigm) for estimating the temporal integration period of any sensory response: the time window during which stimuli alter the response. We apply the TCI method to human electrocorticography (ECoG) and animal physiology recordings to reveal the organization of neural timescales at both the region and single-cell level (Aim I). Pilot data from our analyses reveal that timescales are organized hierarchically, with higher-order regions showing substantially longer integration periods. To explore the functional significance of this timescale hierarchy, we couple TCI with computational techniques well-suited for characterizing natural sounds (Aim II). We test whether increased integration periods enable a more noise-robust representation of speech (Aim IIA), whether regions with longer integration periods code higher-order properties of natural sounds (Aim IIB&IIC), whether there are dedicated integration periods for important sounds categories like speech or music (Aim IID), and whether cortical integration periods can be explained by the duration of the features they respond to (Aim IIE). In the process of conducting this research, I will be trained in two critical areas: (1) ECoG, which is the only method with the spatial and temporal precision to understand how neural timescales are organized in the human brain (2) deep neural networks (DNN) which are the only models able to perform challenging perceptual tasks at human levels and predict neural responses in higher-order cortical regions. After completing this training, I will have a unique set of experimental (fMRI, ECoG, psychophysics) and computational skills (data-driven statistical modeling and hypothesis-driven DNN modeling), which will facilitate my transition to an independent investigator.