Revealing the organization and functional significance of neural timescales inauditory cortex

揭示听觉皮层神经时间尺度的组织和功能意义

• 批准号: 10606820
• 负责人: Samuel V Norman-Haignere
• 金额: $ 24.91万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10606820
• 关键词: Address; Animals; Area; Auditory area; Biomedical Engineering; Brain; Categories; Cells; Code; Collaborations; Communication; Complex; Computational Technique; Computing Methodologies; Data; Data Set; Electrocorticogram; Engineering; Family member; Functional Magnetic Resonance Imaging; Goals; Grant; Human; Learning; Measures; Methods; Modeling; Music; Neural Network Simulation; Neurosciences; Noise; Pathway Analysis; Persons; Physiology; Population; Process; Property; Psychophysics; Reaction Time; Research; Research Personnel; Research Training; Restaurants; Reverse engineering; Sensory; Speech; Speech Perception; Statistical Models; Stimulus; Structure; System; Techniques; Testing; Training; Voice; Work; clinically significant; deep neural network; experience; experimental study; hearing impairment; millisecond; neuromechanism; programs; receptive field; relating to nervous system; response; skills; sound; theories

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.

项目摘要 人们非常擅长通过声音理解世界：理解噪音中的言语 餐厅，挑选家庭成员的声音，或意识到熟悉的旋律。虽然我们接受这些 能力是理所当然的，它们反映了生物工程的令人印象深刻的计算壮举 在机器系统中很难复制。我的研究计划的长期目标是开发计算 和实验方法来反向工程师脑编码自然声音如何像语音和利用 这些进步是了解和帮助治疗听力障碍。核心挑战之一 编码自然声音是它们在从毫秒到几秒钟的许多不同时间尺度上构造 甚至几分钟。大脑如何整合这些潜水时间尺度以从声音中获得意义？ 回答这个问题一直是挑战，因为没有通用方法来衡量 大脑中的神经时间大小。结果，我们对神经时间标准的了解相对较少 在听觉皮层中组织，该组织如何实现自然声音的编码。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） 估计任何感觉响应的临时整合周期：刺激变化的时间窗口 反应。我们将TCI方法应用于人类电视学（ECOG）和动物生理学 录音以揭示该区域和单细胞水平的神经时间尺度的组织（AIM I）。飞行员 来自我们分析的数据表明，时间尺度是按层次结构组织的，高阶区域显示 集成周期大大较长。探讨该时间表层次结构的功能意义，我们 夫妇TCI与非常适合表征自然声音的计算技术（AIM II）。我们测试是否 增加的集成周期可以使语音的更具噪音代表（AIM IIA），是否有区域 较长的集成周期代码自然声音的高阶属性（AIM IIB和IIC），是否有 对于语音或音乐等重要声音类别（AIM IID）等重要声音类别的专用集成期，以及是否是 皮质整合周期可以用它们响应的功能的持续时间来解释（AIM IIE）。在 进行这项研究的过程，我将在两个关键领域进行培训：（1）ECOG，这是唯一的方法 具有空间和临时精度，以了解如何在人脑中组织神经时间尺度 （2）深神经网络（DNN），这是唯一能够执行挑战感知任务的模型 人类水平并预测高阶皮质区域的神经反应。完成此培训后，我将 具有独特的实验性（fMRI，ECOG，心理物理学）和计算技能（数据驱动统计） 建模和假设驱动的DNN建模），这将促进我向独立研究者的过渡。