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，但治疗装置通常是无效的或具有不利的效果。 副作用.下一代闭环神经调节系统将为 通过感测神经状态和调整神经调节作用来改善治疗结果。 这些系统将提供强大的工具，通过阐明治疗机制， 调节生理状态和治疗或行为结果之间的因果关系。 然而，缺乏最佳控制神经刺激的系统方法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网络构成了重大挑战。 将系统接口到具有有限电源的可植入或可穿戴设备中。 拟议的项目通过开发一个开源端到端平台来解决这两个问题， 名为NeuroWeaver，设计，测试和部署智能闭环神经调节（iCLON）系统 它可以通过与神经系统的交互来自动学习最佳的神经调节控制策略， 系统我们将优化神经调节的问题转化为基于奖励的学习， 所需的神经状态或治疗结果代表iCLON系统的奖励的量度。 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系统能够学习最佳行动以最大化其回报。 记忆功能障碍是阿尔茨海默病最具破坏性的症状之一， 痴呆我们将在设计iCLON系统的背景下开发NeuroWeaver平台， 通过闭环杏仁核刺激在海马体中形成良好的记忆状态。优化内存- 增强杏仁核刺激的效果将对研究治疗 记忆障碍更广泛地说，NeuroWeaver平台可以与各种各样的 生物传感器和执行器设计智能闭环控制系统， 生理过程远远超出了本提案中提出的应用。我们提出的平台将 有潜力为机器学习之间的协作创建一个开源生态系统， 神经科学和计算机架构社区，并提供工具，进一步丰富 算法和更广泛的利用在生物医学领域。

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.