Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of artificial intelligence (AI) holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This could lead to a new breed of closed-loop responsive and personalised feedback, which we describe as Neuromorphic Neuromodulation. This can empower precise and adaptive modulation strategies by integrating neuromorphic AI as tightly as possible to the site of the sensors and stimulators. This paper presents a perspective on the potential of Neuromorphic Neuromodulation, emphasizing its capacity to revolutionize implantable brain-machine microsystems and significantly improve patient-specificity.

翻译：神经调控技术已成为治疗多种神经系统疾病的前沿方法，通过精准递送电刺激来调节异常神经元活动。尽管利用人工智能的独特能力实现响应性神经刺激具有巨大潜力，但实时（低延迟）处理、低功耗和散热限制等制约因素使其成为极具挑战性的课题。采用基于人工智能的复杂模型实现个性化神经调控，依赖于通过回传数据至外部系统（例如基于云的医疗中间系统和生态系统）。虽然这可以成为一种解决方案，但将持续学习功能集成到植入式神经调控设备中（例如用于癫痫发作预测等应用）仍是一个悬而未决的问题。我们认为，神经形态架构在神经信号的片上精细分析和人工智能驱动的个性化治疗方面具有开创性潜力。通过将数据处理与特征提取所需的总数据量降低三个数量级以上，神经形态计算在硬件-固件协同设计方面的高能效与存储效率优势，可被视为解决资源受限型植入式神经调控系统的潜在方案。这有望催生新型闭环响应式个性化反馈机制，我们将其定义为"神经形态神经调控"。通过将神经形态人工智能尽可能紧密地集成至传感器和刺激器作用位点，该方法可实现精确的自适应调控策略。本文阐述了神经形态神经调控的潜力，强调其能够彻底变革植入式脑机微系统并显著提升患者个体化治疗效果。