A framework for developing translatable intelligent neural interface systems for precision neuromodulation therapies

尽管神经调节技术取得了进步，但治疗设备往往无效或有不良反应。 副作用。下一代闭环式神经调节系统将为 通过感知神经状态和适应神经调节作用来改善治疗结果。 这些系统将为了解治疗机制提供强大的工具，通过阐明 调节生理状态与治疗或行为结果之间的因果联系。 然而，缺乏系统的方法来优化控制神经刺激是充分利用的主要障碍。 他们的潜力。此外，先进的优化和控制算法的实时实施 需要强大的计算硬件，这对翻译有效的神经构成了重大挑战 将接口系统连接到电源有限的可植入或可穿戴设备。 拟议的项目正在通过开发开放源码的端到端平台来解决这两个问题， 名为NeuroWeaver，用于设计、测试和部署智能闭环神经调制(ICLON)系统 它可以通过与紧张的人互动，自动学习最佳的神经调节控制策略 系统。我们把优化神经调节的问题转化为基于奖励的学习，在那里实现 期望的神经状态或治疗结果代表了对iCLON系统的奖励措施。 我们将使用强化学习和模型预测控制技术来开发算法 使iCLON系统能够学习最佳操作以最大化其回报。 记忆障碍是阿尔茨海默病最具破坏性的症状之一，与年龄有关 痴呆症。我们将在设计iCLON系统的背景下开发NeuroWeaver平台，以诱导 通过闭合环杏仁核刺激使海马区处于良好的记忆状态。优化内存- 增强杏仁核刺激的效果将对治疗的研究产生立竿见影的效果 记忆障碍。更广泛地说，NeuroWeaver平台可以与多种 生物传感器和执行器设计智能闭环调节控制系统 生理过程远远超出了本提案中提议的应用范围。我们建议的平台将 有可能创建一个开源生态系统，用于机器学习、 神经科学和计算机体系结构社区，以及为进一步丰富 算法和在生物医学领域的更广泛应用。

Despite advances in neuromodulation technology, therapeutic devices are often ineffective or have adverse side effects. Next-generation closed-loop neuromodulation systems will provide great potentials for improving the therapeutic outcome by sensing the neural states and adapting the neuromodulatory actions. These systems will provide powerful tools for understanding the mechanisms of treatment by elucidating the causal link between regulating the physiological states and the therapeutic or the behavioral outcomes. However, a lack of systematic approach to optimally control neurostimulation is a major barrier to fully utilize their potentials. Furthermore, the real-time implementation of advanced optimization and control algorithms requires powerful computing hardware that pose a major challenge for translating the effective neural interface systems into implantable or wearable devices with limited power supply. The proposed project is addressing these two problems by developing an open-source end-to-end platform, called NeuroWeaver, to design, test and deploy intelligent Closed-Loop Neuromodulation (iCLON) systems that automatically can learn the optimal neuromodulation control policies by interacting with the nervous system. We cast the problem of optimizing neuromodulation into reward-based learning where achieving the desired neural state or the therapeutic outcome represents a measure of reward for the iCLON system. We will use techniques from reinforcement learning and model predictive control to develop algorithms that enable iCLON systems learn the optimal actions to maximize their reward. Memory dysfunction is one of the most devastating symptoms of Alzheimer’s disease and age-related dementia. We will develop the NeuroWeaver platform in the context of designing iCLON systems to induce good memory states in the hippocampus by closed-loop amygdala stimulation. Optimizing the memory- enhancing effects of amygdala stimulation will have immediate benefits to research on treatments for memory disorders. More broadly, the NeuroWeaver platform can be combined with a wide range of biological sensors and actuators to design intelligent closed-loop control systems for regulating physiological processes far beyond the proposed application in this proposal. Our proposed platform will have the potential to create an open-source ecosystem for collaboration between machine learning, neuroscience, and computer architecture communities as well as provide tools for further enrichment of the algorithms and broader utilization in the biomedical domain.