An Open Source Simulator for Multi Degree-Of-Freedom Brain-Machine Interfaces

项目总结 对于数百万患有运动障碍(包括瘫痪和肌萎缩侧索硬化症)的人来说，皮质内脑机接口(BMI) 是一项旨在恢复失去的运动功能和沟通的新兴技术。主要组成部分 BMI是一种解码算法，它将大脑运动区的神经活动转化为运动学 一种假肢装置。由于这些系统的复杂性，其中包括BMI用户与 在闭合反馈环中解码运动学，目前的技术需要昂贵的侵入性实验 设计、优化和验证解码器算法。对这种实验的需要(1)导致发展缓慢-- 译码算法的测试和评估，以及(2)限制了可以研究这些问题的人的范围 一小群非人类灵长类动物和临床试验实验室。因此，BMI仍处于试点阶段 自2004年他们的fi首次报告演示以来的临床试验。 我们提出了一个新的开源的多自由度BMI系统模拟器。这样做的目的是 模拟器用于(1)将评估和优化BMI算法所需的时间从几个月减少到几分钟， 以及(2)Signifi可以扩大为BMI开发可测试算法的研究人员社区。要建造 在模拟器中，我们提出了神经编码模型，该模型生成多自由度的合成运动皮质活动 任务。这是可能的，因为神经种群活动是相对低维的，具有动力学，这 可以通过递归神经网络(RNN)学习。我们使用从以下方面收集的数据来构建神经模拟器 点对点多自由度到达期间的人体临床试验。我们还建议开发新的人类模型 控制器。这解决了BMI中的一个重要问题：用户在控制 具体的BMI译码算法。我们的模拟器使用深度模仿和强化学习来解决这个问题 有问题。通过模仿学习，它受到限制，不能像人一样执行动作。它通过以下方式进行优化 强化学习探索新策略--在与人类相似的约束下--以实现最优控制 体重指数。总而言之，我们预计这些创新将产生一个纯粹的软件模拟器，可以准确地预测 BMI性能，并支持设计和优化。这个工具将是开源的，所有人都可以使用， 使BMI得以广泛发展。

PROJECT SUMMARY For millions with movement disorders including paralysis and ALS, intracortical brain-machine interfaces (BMIs) are an emerging technology that aims to restore lost motor function and communication. The main component of a BMI is a decoder algorithm that translates neural activity from motor areas of the brain into the kinematics of a prosthetic device. Due to the complexity of these systems, which includes the BMI user interacting with the decoded kinematics in a closed-feedback loop, current technology requires expensive and invasive experiments to design, optimize, and validate decoder algorithms. The need for such experiments (1) results in slow develop- ment and evaluation of decoder algorithms, and (2) limits the scope of people who can work on these problems to a small group of nonhuman primate and clinical trial labs. As a consequence, BMIs have remained in pilot clinical trials since their first reported demonstration in 2004. We propose a new open-source simulator for multiple degree-of-freedom (DOF) BMI systems. The goals of this simulator are to (1) reduce the time it takes to evaluate and optimize BMI algorithms from months to minutes, and (2) significantly expand the community of researchers who develop testable algorithms for BMIs. To build the simulator, we propose neural encoding models that generate synthetic motor cortical activity for multiple DOF tasks. This is possible because neural population activity is relatively low-dimensional and has dynamics, which can be learned via recurrent neural networks (RNNs). We build our neural simulators using data collected from human clinical trials during point-to-point multi-DOF reaches. We also propose to develop new models of human controllers. This solves an important problem in BMIs: users learn new control strategies when controlling a particular BMI decoder algorithm. Our simulator uses deep imitation and reinforcement learning to solve this problem. It is constrained through imitation learning to perform actions like a human. It is optimized through reinforcement learning to explore new strategies – under the constraint of being human-like – to optimally control the BMI. Together, we expect these innovations will result in a purely software simulator that accurately predicts BMI performance and enables design and optimization. This tool will be open-sourced and available to all, enabling widespread development of BMIs.