NCS-FO: Unraveling Cortical Circuits for Auditory Scene Analysis

NCS-FO：揭示听觉场景分析的皮层回路

• 批准号: 1835270
• 负责人: Kamal Sen
• 金额: $ 99.97万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835270&HistoricalAwards=false
• 关键词: NCS; FO; Unraveling; Cortical; Circuits

In everyday social situations, normal hearing humans are able to listen to a speaker in the midst of other people talking and other sound sources. This is an example of a general problem termed auditory scene analysis. The understanding of auditory scene analysis remains a challenging problem in a diverse range of fields such as neuroscience, computer science, speech recognition and engineering, after more than 50 years of research. Although a difficult problem for machines and hearing impaired listeners, humans with normal hearing solve it with relative ease. This suggests the existence of a solution to this problem in the brain, but this solution remains unknown. This project will investigate how the brain solves this problem. By revealing circuits in the brain that contribute to the solution, this project will ultimately improve quality of life through applications in medical devices, e.g., hearing aids and cochlear implants; benefit society through applications in technology, e.g., applications for speech recognition; and create an educational platform to train students to integrate knowledge from a variety of disciplines to address challenging and important societal problems.The spatial location of different sound sources is an important component of auditory scene analysis. The auditory cortex, with its unique spatial sound processing ability, is thought to play an important role in auditory scene analysis, although the underlying neural network mechanisms remain largely unknown. This project will investigate cortical circuits for auditory scene analysis in the primary auditory cortex of the mouse, employing powerful optogenetic tools to investigate both bottom-up and top-down mechanisms. First, the investigators will examine the influence of behavioral states on cortical spatial representations of sound mixtures across different cortical layers and test the hypotheses that such spatial representations vary across cortical layers and behavioral states of the animal. Second, the investigators will examine the causal role of parvalbumin positive (PV) inhibitory interneurons versus somatostatin positive (SOM) inhibitory interneurons in the primary auditory cortex, using optogenetic manipulations, and test the hypothesis that PV interneurons are critical in mediating bottom-up signaling, whereas SOM interneurons are selectively engaged during active behavioral states. Finally, the investigators will construct a computational model of the primary auditory cortex including excitatory, PV and SOM neurons to explain the experimental results and make predictions for future experiments.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.

在日常社交场合中，正常听力的人能够在其他人说话和其他声源中听到说话者的声音。这是称为听觉场景分析的一般问题的示例。经过 50 多年的研究，对听觉场景分析的理解在神经科学、计算机科学、语音识别和工程等各个领域仍然是一个具有挑战性的问题。尽管对于机器和听力受损的听众来说这是一个难题，但听力正常的人相对容易解决它。这表明大脑中存在解决该问题的方法，但该解决方案仍然未知。该项目将研究大脑如何解决这个问题。通过揭示大脑中有助于解决方案的电路，该项目最终将通过在医疗设备（例如助听器和人工耳蜗）中的应用来提高生活质量；通过技术应用造福社会，例如语音识别的应用；并创建一个教育平台，培养学生整合多种学科的知识，以解决具有挑战性的重要社会问题。不同声源的空间位置是听觉场景分析的重要组成部分。听觉皮层以其独特的空间声音处理能力，被认为在听觉场景分析中发挥着重要作用，尽管其潜在的神经网络机制仍然很大程度上未知。该项目将研究小鼠初级听觉皮层中用于听觉场景分析的皮层回路，采用强大的光遗传学工具来研究自下而上和自上而下的机制。首先，研究人员将检查行为状态对不同皮层层声音混合物的皮层空间表征的影响，并测试这种空间表征在动物皮层和行为状态之间变化的假设。其次，研究人员将使用光遗传学操作来检查初级听觉皮层中小清蛋白阳性（PV）抑制性中间神经元与生长抑素阳性（SOM）抑制性中间神经元之间的因果作用，并检验 PV 中间神经元在介导自下而上信号传导中至关重要的假设，而 SOM 中间神经元在活跃的行为状态下有选择性地参与。最后，研究人员将构建包括兴奋性、PV 和 SOM 神经元在内的初级听觉皮层的计算模型，以解释实验结果并为未来的实验做出预测。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

AIM: A network model of attention in auditory cortex

目的：听觉皮层注意力的网络模型

• DOI: 10.1371/journal.pcbi.1009356
• 发表时间: 2021-08
• 期刊: PLOS Computational Biology
• 作者: Chou KF;Sen K
• 通讯作者: Sen K