NCS-FR: Engineering Brain Circuits for Complex Scene Analysis

NCS-FR：用于复杂场景分析的工程大脑电路

• 批准号: 2319321
• 负责人: Kamal Sen
• 金额: $ 296.19万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319321&HistoricalAwards=false
• 关键词: NCS; FR; Engineering; Brain; Circuits

Everyday social situations, like a crowded party, a restaurant, a classroom, or an open-plan workplaces, involve multiple speakers and listeners and the hum of background noise. In these complex sound environments, humans with typical hearing are able to identify and listening to individual sound sources, for example what a single speaker is saying, while ignoring the other sound sources, for example someone else's phone call or background noise, like cars driving down the street. This is an example of a general problem called complex scene analysis (CSA), and a full understanding of how humans with typical hearing solve this problem has remained elusive to scientists from a diverse range of fields - neuroscience, computer science, speech recognition and engineering - even after more than 50 years of research. Because of this, CSA remains a problem for many humans, like those with hearing impairment, for medical devices, like hearing aids, and for technology, for example automatic speech recognition systems. This project investigates the neural basis of complex scene analysis in typical hearing, and, based on these discoveries, develops a brain inspired algorithm for CSA. This project will ultimately improve quality of life through a variety of applications, for example for improving the effectiveness of hearing aids and speech recognition technologies. Solving this problem requires an interdisciplinary effort, and as part of the research, an educational platform is developed to train students to integrate knowledge from a variety of disciplines that makes them better able to address challenging and important societal problems. This project integrates three interdisciplinary research threads to develop the brain-inspired algorithm. The first thread uses brain imaging in humans performing CSA with an integrated wearable device that measures brain signals (functional near-infrared spectroscopy and electroencephalography), and machine learning methods to decode where a subject is attending in a complex audiovisual scene. The second thread investigates cortical circuits for CSA in attentive states, which are thought to enhance CSA performance. This thread integrates electrophysiology, optogenetics, behavior and computational modeling in mice, a model system with well-established, powerful experimental tools for unraveling cortical circuits. The third thread designs an attention steered algorithm for the wearable device that selectively processes an attended source in a complex scene, integrating the attended location decoded from a subject’s brain signals (thread 1), and a model of cortical circuits in attentive states (thread 2). This thread optimizes the algorithm to generate a fast, compact, energy efficient, and state of the art algorithm for CSA and evaluate its performance in humans.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.

日常社交场合，例如拥挤的聚会、餐厅、教室或开放式工作场所，都会涉及多个扬声器和听众以及背景噪音，在这些复杂的声音环境中，具有正常听力的人能够识别和聆听。忽略其他声源，例如其他人的电话或背景噪音，例如街道上行驶的汽车。这是一个称为复杂场景分析的常见问题的示例。 （CSA），以及对人类如何即使经过 50 多年的研究，对于神经科学、计算机科学、语音识别和工程学等不同领域的科学家来说，用典型的听力解决这个问题仍然是一个难题。因此，CSA 对于许多人来说仍然是一个问题。该项目研究了典型听力中复杂场景分析的神经基础，并根据这些发现开发了一种受大脑启发的算法。该项目最终将通过各种应用来提高生活质量，例如提高助听器和语音识别技术的有效性。解决这个问题需要跨学科的努力，作为研究的一部分，开发了一个教育平台来培训学生整合知识。该项目整合了三个跨学科的研究线索，以开发受大脑启发的算法，第一个线索使用人类的大脑成像，通过集成的可穿戴设备进行 CSA 测量。大脑信号（功能性近红外）第二条线索研究了注意力状态下 CSA 的皮质回路，该线索整合了电生理学、光遗传学、行为增强。第三个线程为可穿戴设备设计了一种注意力引导算法，可以选择性地处理数据。复杂场景中的参与源，集成从受试者的大脑信号解码的参与位置（线程 1）以及注意力状态下的皮质电路模型（线程 2）。该线程优化了算法，以生成快速、紧凑、节能的算法。 ，以及最先进的 CSA 算法，并评估其在人类中的表现。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进行评估，被认为值得支持。