EFRI BRAID: Optical Neural Co-Processors for Predictive and Adaptive Brain Restoration and Augmentation

EFRI BRAID：用于预测性和适应性大脑恢复和增强的光学神经协处理器

• 批准号: 2223495
• 负责人: Arka Majumdar
• 金额: $ 197.04万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223495&HistoricalAwards=false
• 关键词: EFRI; BRAID; Optical; Neural; Co; EFRI; BRAID; Optical; Neural; Co

Neurological disorders, such as traumatic brain injury, stroke, or cerebral palsy, are an important cause of disability and death worldwide. Nearly one in six of the world’s population experience these disorders. However, the very limited treatments available for these disorders provide only modest therapeutic benefits and are often associated with serious side effects. Brain-inspired, implanted computing devices could provide a solution for rehabilitating and curing these disorders. Such devices can operate by recording electrical signals from the nervous system, processing them, and stimulating another part of the brain in real-time. This allows the injured or impaired area of the brain to be bypassed or rehabilitated. However, existing brain-inspired computing devices consume too much power and are not fast enough to provide such real-time feedback and control. This project aims to create a “brain co-processor” by innovating in two aspects: first, create new algorithms based on neural signals collected from the brain to provide higher accuracy; and second, by employing optical hardware that not only can process information with high speed and low power, but also directly interfaces with the brain by exploiting light-controlled proteins in the brain. Furthermore, this project aims to improve the training and education of undergraduate and high school students, with a strong emphasis on including women and underrepresented minority communities, in multi-disciplinary research on optics, machine learning, and neuroscience. The scientific results will be disseminated to a wide scientific audience via seminars, workshops, peer-reviewed publications, and conferences.Understanding how the brain works and using that knowledge to restore or augment brain function require ultrafast parallel algorithms that are orders-of-magnitude more advanced than current state-of-the-art. This research project will build “optical neural co-processors” that use light as a computational resource and leverage brain-inspired encoder-decoder recurrent neural networks to interact with the brain in multiple natural timescales of the brain. Combining expertise in theoretical neuroscience, neuro-inspired machine learning, optogenetics, neuro-rehabilitation, nanophotonics and integrated semiconductor optics, this research project will develop brain-inspired predictive coding artificial neural networks for neural interfacing and co-processing; design and fabricate optical neural architectures that exploit emerging semiconductor nanophotonics and integrated photonics; as well as demonstrate optical neural co-processors that interface with the brain in real-time for rehabilitation in non-human primates. Along with technical advancements, neuro-ethical implications of the developed technologies will be investigated in this project.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.

神经系统疾病，例如脑损伤，中风或脑瘫，是全球疾病和死亡的重要原因。世界上近六分之一的人口经历了这些疾病。但是，可用于这些疾病的非常有限的治疗方法仅提供适度的治疗益处，并且通常与严重的副作用有关。受脑启发的，植入的计算设备可以为修复和治愈这些疾病提供解决方案。这些设备可以通过记录神经系统的电信号，处理它们并实时刺激大脑的另一部分来操作。这允许大脑受伤或受损的区域被绕过或修复。但是，现有的脑启发设备会消耗过多的功率，并且不足以提供这种实时反馈和控制。该项目旨在通过在两个方面引入“大脑协调员”来创建“大脑协会”：首先，基于从大脑收集的神经信号创建新算法以提供更高的准确性；其次，通过采用光学硬件，不仅可以通过高速和低功率处理信息，还可以通过利用大脑中的光控制蛋白来直接与大脑接口。此外，该项目旨在改善本科和高中生的培训和教育，并强调包括妇女和代表性不足的少数民族社区，在有关光学，机器学习和神经科学的多学科研究中。科学的结果将通过半手，讲习班，同行评审的出版物和会议传播给广泛的科学观众。理解大脑的工作方式并利用这些知识来恢复或增强大脑功能需要超级平行的算法，这些算法是比当前的阶段更高级的阶级。该研究项目将建立“光学神经协会”，该项目使用光作为计算资源，并利用脑启发的编码器复发器复发性神经元网络在大脑的多个天然时间表中与大脑相互作用。该研究项目结合了理论神经科学，神经启发的机器学习，光遗传学，神经理化，纳米光学学和集成的半导体光学的专业知识，该研究项目将开发出脑启发的预测性编码人工神经网络，以进行神经介入和共同处理；设计和制造光学神经结构，可利用新兴的半导体纳米光子学和集成的照片；还展示了光学神经元的处理器，可实时与大脑接触以进行非人类隐私的康复。随着技术进步，将在该项目中调查开发技术的神经伦理含义。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准通过评估来支持的。