Ghost in the Machine: Melding Brain, Computer and Behavior

机器中的幽灵：大脑、计算机和行为的融合

• 批准号: 10012013
• 负责人: Brian Litt
• 金额: $ 113.4万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10012013
• 关键词: Affect; Area; Behavior; Brain; Caring; Clinical; Communication; Computers; Coupling; Data; Devices; Disease; Educational process of instructing; Effectiveness; Electric Stimulation; Engineering; Epilepsy; Feeling; Generations; Goals; Health; Human; Implant; Intervention; Investigation; Knowledge; Learning; Life; Link; Machine Learning; Measurement; Measures; Methods; Natural Language Processing; Neurologic; Neurology; Office Visits; Patients; Pattern; Peripheral; Physicians; Play; Process; Psychological reinforcement; Quality of life; Role; Sampling; Spinal Cord; Stream; Techniques; Technology; Therapeutic; Thinking; United States National Institutes of Health; User-Computer Interface; Work; blind; brain computer interface; brain machine interface; cloud based; computer science; convolutional neural network; deep learning; experience; implantable device; improved; intelligent algorithm; learned behavior; learning algorithm; nervous system disorder; next generation; novel; relating to nervous system; sensor; tool

Implantable devices are playing a greater role in neurologic care, but their effectiveness is limited, because they are blind to human thoughts, feelings, and behavior – factors that most dramatically affect our health. Coupling peripheral sensors to implants might help, but wouldn’t it be easier if the devices just asked us? Armed with this knowledge, next generation machines will more effectively drive neural activity in the brain to healthy states. They will also quickly learn behaviors that worsen health and guide us to better choices. Though DARPA, the NIH, and Neuralink are spending millions of dollars on new hardware for brain-computer interfaces, none focus on reciprocal, natural communication between host and machine. There is a desperate need for novel, practical methods that enable devices to learn from and guide human behavior. In this application I propose to develop a new generation of autonomous brain-machine interfaces – devices that can question, record, act - and combine learning algorithms applied to neurosignals with teaching by their human hosts. Life with these implants will entail a subtle human- machine dialogue in which devices and humans teach and learn from each other. Humans will inform intelligent algorithms about what we are doing and feeling, while machines will incorporate this information into therapy and guide us to optimize quality of life in personalized ways. This is a paradigm shift from today’s simple devices, which are programmed by physicians during occasional office visits. I propose to demonstrate this paradigm in a practical, scalable way using current epilepsy implants that is rapidly translatable to many neurological disorders. To achieve this goal, I will meld several cutting-edge technologies in novel ways, including: (1) State-of-the-art, high bandwidth implantables that sample neural activity, link to vast cloud- based computational power to process it, and intervene to modulate brain, spinal cord or peripheral neural activity. This work utilizes my experience from the past 20 years; (2) I will deploy powerful new computer science tools in novel ways. I will use convolutional neural nets (a.k.a. Deep Learning) to learn patterns from vast streams of continuous high-bandwidth neural data, build a two way human-machine interface using Natural Language Processing (NLP)., and probe networks with changes in human behavior and electrical stimulation and guide interventions toward therapeutic goals using Reinforcement Learning. Combining these computer science, machine learning techniques and measurements of human behavior is a new area of investigation for me that will leverage my unique background in clinical neurology and engineering to build a new class of interactive, human therapeutic devices.

植入式设备在神经护理中发挥着更大的作用，但其有效性 有限的，因为他们对人类的思想，感情和行为视而不见-大多数人 严重影响我们的健康。将外围传感器连接到植入物上可能会有所帮助， 更简单的方法吗有了这些知识，下一代机器将更加 有效地驱动大脑中的神经活动到健康状态。他们也会很快学会行为 这会使我们的健康恶化，并引导我们做出更好的选择。尽管DARPA、NIH和Neuralink 花费数百万美元用于脑机接口的新硬件，没有人关注 主机和机器之间的相互、自然的通信。我们迫切需要 新的、实用的方法，使设备能够从人类行为中学习并指导人类行为。 在这个应用程序中，我建议开发新一代的自主脑机 接口-可以提问，记录，行动-和联合收割机学习算法的设备， 神经信号和人类宿主的教导。带着这些植入物的生活将需要一个微妙的人类- 机器对话，设备和人类相互教和学。人类将告知 智能算法，我们正在做什么和感觉，而机器将纳入这 信息融入治疗，并指导我们以个性化的方式优化生活质量。这是一 范式转变，从今天的简单设备，这是由医生编程，在偶尔 办公室访问。我建议使用当前的 癫痫植入物可迅速转化为许多神经系统疾病。 为了实现这一目标，我将以新颖的方式融合几项尖端技术，包括： (1)最先进的高带宽植入式设备，可以对神经活动进行采样，连接到巨大的云- 基于计算能力来处理它，并干预调节大脑，脊髓或外周 神经活动这项工作利用了我过去20年的经验;（2）我将部署强大的 新的计算机科学工具。我将使用卷积神经网络（convolutional neural nets）。深 学习）从大量连续的高带宽神经数据流中学习模式， 使用自然语言处理（NLP）的双向人机界面。，和探针 网络与人类行为和电刺激的变化，并指导干预， 使用强化学习实现治疗目标。结合这些计算机科学，机器 学习人类行为的技术和测量对我来说是一个新的研究领域 利用我在临床神经学和工程学方面的独特背景， 互动的人类治疗设备。