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

基本信息

批准号：
10704095
负责人：
Brian Litt
金额：
$ 113.75万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
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/10704095
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 empowerment experience implantable device improved intelligent algorithm learned behavior learning algorithm nervous system disorder neural next generation novel sensor tool

项目摘要

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.

植入式设备在神经科护理中发挥着越来越大的作用，但其有效性还很有限有限，因为他们对人类的思想、感情和行为视而不见——这些因素是最重要的将外围传感器与植入物耦合可能会有所帮助，但不是吗？如果设备只是问我们，那么下一代机器会更容易吗？他们还将快速学习行为尽管 DARPA、NIH 和 Neuralink 是这样，但它可以延长健康并引导我们做出更好的选择。花费数百万美元购买脑机接口的新硬件，但没有一个专注于主机和机器之间迫切需要相互的、自然的通信。新颖、实用的方法使设备能够学习并指导人类行为。在这个应用中，我建议开发新一代自主脑机接口——可以提问、记录、行动的设备——并将学习算法结合起来这些植入物的生活将带来微妙的人性化。机器对话，设备和人类互相教导和学习。关于我们正在做什么和正在感受的智能算法，而机器将整合这些将信息融入治疗并指导我们以个性化的方式优化生活质量。当今简单设备的范式转变，这些设备由医生偶尔编程我建议利用当前的实际、可扩展的方式来展示这一范例。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多项尖端技术，包括： (1) 最先进的高带宽植入物，可对神经活动进行采样，链接到巨大的云- 基于计算能力来处理它，并进行干预以调节大脑、脊髓或外周血管这项工作利用了我过去20年的经验；（2）我将部署强大的神经活动；我将以新颖的方式使用卷积神经网络（又名深度）。学习）从大量连续高带宽神经数据流中学习模式，构建使用自然语言处理（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结合这些计算机科学、机器来实现治疗目标。学习技术和人类行为的测量对我来说是一个新的研究领域这将利用我在临床神经病学和工程学方面的独特背景来建立一个新班级交互、人体治疗设备。