CAREER: Transforming Implantable Neural Interfaces through Computing: From Circuits to Systems

职业：通过计算改变植入式神经接口：从电路到系统

• 批准号: 2317764
• 负责人: Visvesh Sathe
• 金额: $ 51.41万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2317764&HistoricalAwards=false
• 关键词: CAREER; Transforming; Implantable; Neural; Interfaces

Understanding and engineering brain function has been identified as a grand scientific challenge in recognition of its potential to revolutionize a number of fields including computing and medicine. Fully-implantable Bidirectional Brain Computer Interfaces (BBCI) are the focus of this project. These are electronic systems capable of recording, processing and stimulating neural activity, and they play a foundational role in enabling better understanding of the brain. Implantable BBCIs will enable neuro-scientists to explore brain function in unprecedented detail, and help realize neuro-prosthetic devices capable of restoring mobility, vision and brain function among the disabled. However, critical technological barriers facing BBCIs are hindering progress in neuroscience. Existing BBCI architectures do not scale well, in terms of power or area, to support ever-increasing numbers of recording and stimulation channels needed for finer examination and control of brain function. Furthermore, neural stimulation produces artifacts - electrical disturbances in the brain - that hamper the ability to perform neural recording. Finally, the desired level of computational performance required to perform neural signal processing and data-communication to external devices consumes power in excess of thermal limits of implantable devices. The technologies resulting from this project will be translated into a fully implantable, bio-compatible and versatile closed-loop neuroscience platform that overcomes these existing challenges. Collaborations with neuroscientists, the medical-device industry, and fabrication partners, to be pursued during this project, are critical to the realization of this goal. The resulting platform will be made available to the broader neuroscience community to enable experiments at unprecedented levels of scale and scope, accelerating progress toward understanding the brain. Both graduate and underrepresented minority students will be involved in the project.This award addresses the critical BBCI barriers of power, area, performance and recording quality by investigating and devising cross-cutting technologies that span digital/mixed-signal circuit design, architecture, systems theory and system integration. Exploiting domain-specific structure across every level of abstraction, from algorithm partitioning down to package- and circuit-design is central to this project. The effort is organized into three threads: 1) Development of novel, computationally-enhanced neural interfaces to achieve desired levels of efficiency and scalability; 2) Exploration of domain-correspondence to Multiple-Input Multiple Output (MIMO) communication systems through low-energy computing, which will allow systems capable of rejecting artifacts and allowing recording to be focused to a targeted set of neurons; and 3) Leveraging an understanding of neural signal processing algorithms and preliminary results in ultra-low power computing to devise domain specific architectures that meet BBCI processing requirements under severe power limitations.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.

理解和设计大脑功能已被认为是一项重大科学挑战，因为它有可能彻底改变包括计算和医学在内的许多领域。完全植入式双向脑机接口（BBCI）是该项目的重点。这些电子系统能够记录、处理和刺激神经活动，在更好地理解大脑方面发挥着基础作用。植入式 BBCI 将使神经科学家能够以前所未有的细节探索大脑功能，并帮助实现能够恢复残疾人活动能力、视力和大脑功能的神经假体装置。然而，BBCI 面临的关键技术障碍正在阻碍神经科学的进步。现有的 BBCI 架构在功率或面积方面无法很好地扩展，无法支持对大脑功能进行更精细检查和控制所需的不断增加的记录和刺激通道数量。此外，神经刺激会产生伪影（大脑中的电干扰），从而妨碍执行神经记录的能力。最后，执行神经信号处理和与外部设备的数据通信所需的计算性能水平所消耗的功率超过了可植入设备的热限制。该项目产生的技术将转化为完全可植入、生物相容且多功能的闭环神经科学平台，以克服这些现有的挑战。该项目期间与神经科学家、医疗器械行业和制造合作伙伴的合作对于实现这一目标至关重要。由此产生的平台将提供给更广泛的神经科学界，以前所未有的规模和范围进行实验，加速理解大脑的进展。研究生和代表性不足的少数族裔学生都将参与该项目。该奖项通过研究和设计跨越数字/混合信号电路设计、架构、系统的交叉技术，解决了功率、面积、性能和录音质量方面的关键 BBCI 障碍理论与系统集成。从算法划分到封装和电路设计，跨各个抽象级别利用特定领域的结构是该项目的核心。这项工作分为三个主题：1）开发新颖的计算增强型神经接口，以达到所需的效率和可扩展性水平； 2）通过低能耗计算探索多输入多输出（MIMO）通信系统的域对应关系，这将使系统能够拒绝伪影并允许记录集中到一组目标神经元； 3）利用对神经信号处理算法的理解和超低功耗计算的初步结果，设计出在严格的功耗限制下满足 BBCI 处理要求的特定领域架构。该奖项反映了 NSF 的法定使命，并通过评估认为值得支持利用基金会的智力优势和更广泛的影响审查标准。