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

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，例如创伤性脑损伤、中风或脑瘫，是全世界残疾和死亡的重要原因。然而，针对这些疾病的治疗方法非常有限。受大脑启发的植入式计算设备可以提供治疗益处，并且通常会带来严重的副作用，这些设备可以通过记录神经系统的电信号、处理这些信号并刺激神经系统的另一部分来进行操作。实时大脑。允许绕过或恢复大脑受伤或受损的区域。然而，现有的类脑计算设备消耗太多电量，并且速度不够快，无法提供这种实时反馈和控制。 “大脑协处理器”通过两个方面的创新：一是基于从大脑收集的神经信号创建新的算法，以提供更高的精度；二是采用光学硬件，不仅可以高速、低功耗地处理信息，而且还通过利用直接与大脑交互此外，该项目旨在改善本科生和高中生的培训和教育，重点关注女性和代表性不足的少数群体参与光学、机器学习和光学等多学科研究。科学成果将通过研讨会、讲习班、同行评审出版物和会议传播给广大科学受众。了解大脑如何工作并利用这些知识来恢复或增强大脑功能需要超快的并行算法。 -幅度更大该研究项目将构建“光学神经协处理器”，使用光作为计算资源，并利用受大脑启发的编码器-解码器循环神经网络在多个自然时间尺度与大脑进行交互。该研究项目将结合理论神经科学、神经启发机器学习、光遗传学、神经康复、纳米光子学和集成半导体光学方面的专业知识，开发用于神经接口和大脑启发的预测编码人工神经网络。协同处理；设计和制造利用新兴半导体纳米光子学和集成光子学的光学神经架构；以及展示与大脑实时交互的光学神经协处理器，以实现非人类灵长类动物的康复。该项目将研究所开发技术的神经伦理影响。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。

项目成果

Neural co-processors for restoring brain function: results from a cortical model of grasping

用于恢复大脑功能的神经协处理器：抓取皮质模型的结果

• DOI: 10.1088/1741-2552/accaa9
• 发表时间: 2023-05
• 期刊: Journal of Neural Engineering
• 影响因子: 4
• 作者: Bryan, Matthew J.;Preston Jiang, Linxing;P N Rao, Rajesh
• 通讯作者: P N Rao, Rajesh

Photonic advantage of optical encoders

光学编码器的光子优势

• DOI: 10.1515/nanoph-2023-0579
• 发表时间: 2023-11
• 期刊: Nanophotonics
• 影响因子: 7.5
• 作者: Huang, Luocheng;Tanguy, Quentin A.;Fröch, Johannes E.;Mukherjee, Saswata;Böhringer, Karl F.;Majumdar, Arka
• 通讯作者: Majumdar, Arka

Recursive neural programs: A differentiable framework for learning compositional part-whole hierarchies and image grammars

递归神经程序：用于学习组合部分整体层次结构和图像语法的可微分框架

• DOI: 10.1093/pnasnexus/pgad337
• 发表时间: 2023-11
• 期刊: PNAS Nexus
• 作者: Fisher, Ares;Rao, Rajesh P. N.
• 通讯作者: Rao, Rajesh P. N.