CRCNS US-France Research Proposal: Neural computations of adaptive temporal integration in auditory cortex

CRCNS 美国-法国研究提案：听觉皮层自适应时间整合的神经计算

• 批准号: 2308725
• 负责人: Sam Norman-Haignere
• 金额: $ 76.91万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2028-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2308725&HistoricalAwards=false
• 关键词: CRCNS; US; France; Research; Proposal

People are remarkably skilled at perceiving and understanding complex sounds such as speech and music. Yet, the brain mechanisms that allow people to perceive complex sounds remain poorly understood. A key challenge faced by the brain is that sounds are organized across a vast range of timescales from milliseconds to seconds and minutes. To derive information from sound, the brain must have mechanisms for flexibly integrating information across these diverse timescales. Understanding these mechanisms is critical to understanding how people understand speech, perceive music, and navigate the world using sound. In this project, researchers aim to use computational models to understand how ferret cortex processes multiple types of sound at multiple timescales. The researchers also plan to give lectures, conduct outreach, and mentor undergraduates publicize the results and foster the unique skills needed for this type of cutting-edge research. The proposed project investigates possible mechanisms the brain might use to flexibly integration information. A key question the researchers investigate is whether the brain can change the time window it integrates information over depending on the duration of sound “structures” such as words in speech. They aim to test whether the brain can change its integration window over short and long timescales, and whether this flexibility depends on the types of sounds that one hears and their behavioral relevance. They intend to study these questions using neural recordings from the auditory cortex, a new paradigm for studying temporal integration in the brain, and computational models derived from deep neural networks. This project is funded jointly by the Cognitive Neuroscience Program in the Social, Behavioral, and Economic Sciences Directorate and the Neural Systems Cluster in the Biological Sciences Directorate. A companion project is being funded by the French National Research Agency (ANR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人们在感知和理解复杂的声音（如演讲和音乐）方面非常熟练。然而，让人们感知复杂声音的大脑机制仍然知之甚少。大脑面临的一个关键挑战是，声音是在从毫秒到秒到分钟的巨大时间尺度上组织起来的。为了从声音中获取信息，大脑必须具备灵活整合这些不同时间尺度信息的机制。理解这些机制对于理解人们如何理解语言、感知音乐和利用声音导航世界至关重要。在这个项目中，研究人员旨在使用计算模型来了解雪貂皮层如何在多个时间尺度上处理多种类型的声音。研究人员还计划举办讲座，开展外展活动，并指导本科生宣传结果，培养这种前沿研究所需的独特技能。该项目旨在研究大脑灵活整合信息的可能机制。研究人员调查的一个关键问题是，大脑是否可以根据声音“结构”（如语音中的单词）的持续时间改变整合信息的时间窗口。他们的目的是测试大脑是否可以在短时间和长时间尺度上改变其整合窗口，以及这种灵活性是否取决于人们听到的声音类型及其行为相关性。他们打算使用来自听觉皮层的神经记录来研究这些问题，这是研究大脑时间整合的新范式，以及来自深度神经网络的计算模型。该项目由社会、行为和经济科学理事会的认知神经科学计划和生物科学理事会的神经系统集群共同资助。法国国家研究机构（ANR）正在资助一个伙伴项目。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。