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Collaborative Research: Neural-cognitive analysis of spatial scenes with competing, dynamic sound sources

Collaborative Research: Neural-cognitive analysis of spatial scenes with competing, dynamic sound sources
合作研究:对具有竞争性动态声源的空间场景进行神经认知分析
批准号:
1539376
负责人:
Yi Zhou
金额:
$33.78万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-09-01 至 2019-08-31

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中文摘要
翻译
这个项目研究从混合声源中提取重要信息的神经认知机制。想象一下,有一天,你再也无法分辨正向你驶来的汽车的喇叭声和其他街道上的声音。这种专注于一个声源而忽略其他声源的认知能力对听力障碍者来说是一个日常挑战。虽然检测和定位单一声音的基本神经机制是已知的,但我们不知道大脑如何完成对多个声源的听觉场景分析。到目前为止,研究的重点是啮齿动物和食肉动物较低的大脑中心,而来源分离的神经机制预计将在较高水平上,即听觉皮质。这项研究将记录单个皮质神经元的反应,并在相同的声学场景下进行人体受试者实验。在综合这些结果的基础上,将开发一个功能听觉模型。这将为助听器、社交机器人和监控系统提供新的科学见解和智能算法。该项目将为研究生和本科生提供研究机会,并包括面向高中生和大学生的外联活动和在线学习资源,以提高公众对神经科学的认识。研究成果和模型将与学术界分享。这项建议将使用跨学科的方法,通过将心理声学实验与单一单位电生理学相结合来了解听觉场景分析的核心机制。这项研究将调查听觉系统如何定位时间上嵌入到空间分离的掩蔽物中的目标声音。单单元录音将以听觉皮质的尾部区域为目标,这是复杂声音分析的假定“位置”途径。我们假设皮层活动既代表旧的声音,也代表新的声音,因此可以从整体混合中减去“旧的”掩蔽源的内部表示。这有助于更清晰地感知“新的”目标元素,展示了听觉场景分析中的一种基本心理物理现象。为了验证这一假设,我们将分别和组合识别单个声源的神经信号。然后,我们将基于从多个运动和静止声源的声音定位测试中获得的感知数据来解释这些信号。发现听觉场景分析的基本大脑机制将为我们提供新的神经生理学洞察力,并为声源分离提供潜在的技术解决方案。
英文摘要
This project investigates neurocognitive mechanisms that extract important information from a mixture of sound sources. Imagine a day where you could no longer distinguish the honking horn of a car coming right at you from other street sounds. This cognitive ability to attend to one sound source while ignoring others presents an everyday challenge for people with hearing impairments. While the basic neural mechanisms for detecting and localizing single sounds are known, we do not know how the brain accomplishes auditory scene analysis with multiple sound sources. So far, studies have focused on lower brain centers in rodents and carnivores, while the neural mechanisms for source segregation are expected to be at higher levels, in the auditory cortex. This study will record the responses of single cortical neurons and conduct human-subject experiments for the same acoustic scenarios. Based on the integration of these results, a functional auditory model will be developed. This will provide new scientific insights and enable intelligent algorithms for hearing aids, social robotics, and surveillance systems. The project will provide research opportunities for graduate and undergraduate students and include outreach activities and online learning resources for high-school and college students to increase the public awareness of neuroscience. The research results and the model will be shared with the academic community. This proposal will use an interdisciplinary approach to gain understanding of the central mechanisms of auditory scene analysis by integrating psychoacoustical experiments with single-unit electrophysiology. The study will investigate how the auditory system localizes a target sound temporally embedded in a spatially separated masker. Single-unit recording will target the caudal region of the auditory cortex, the putative "where" pathway for complex sound analysis. We hypothesize that cortical activity represents both the old and new sounds, so that the internal representation of the "old" masking source can be subtracted from the overall mixture. This facilitates a clearer perception of the "new" target element, demonstrating a fundamental psychophysical phenomenon within auditory scene analysis. To test this hypothesis, we will identify the neural signals for individual sound sources separately and in combination. We will then interpret these signals based on the perceptual data gained from sound localization tests with multiple moving and stationary sound sources. Discovering the fundamental brain mechanisms for auditory scene analysis will provide new neurophysiological insight into a well-established psychophysical field and offer potential technical solutions for sound-source segregation.
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CAREER: Reinforcement Learning-Based Control of Heterogeneous Multi-Agent Systems in Structured Environments: Algorithms and Complexity
  • 批准号:
    2237830
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $54.1万
  • 财政年份:
    2023
  • 负责人:
    Yi Zhou
  • 依托单位:
Collaborative Research: SCALE MoDL: Advancing Theoretical Minimax Deep Learning: Optimization, Resilience, and Interpretability
  • 批准号:
    2134223
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $57.61万
  • 财政年份:
    2021
  • 负责人:
    Yi Zhou
  • 依托单位:
CIF: Small: Self-Adaptive Optimization Algorithms with Fast Convergence via Geometry-Adapted Hyper-Parameter Scheduling
  • 批准号:
    2106216
  • 项目类别:
    Standard Grant
  • 资助金额:
    $41.12万
  • 财政年份:
    2021
  • 负责人:
    Yi Zhou
  • 依托单位:
国内基金
海外基金
Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    SATOSHI NAWATA
  • 依托单位:
Cell Research
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