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 perspective introduces the concept of Neuromorphic Neuromodulation, a new breed of closed-loop responsive feedback system. It highlights its potential to revolutionize implantable brain-machine microsystems for patient-specific treatment

翻译：神经调控技术通过精确递送电刺激来调节异常神经元活动，已成为治疗多种神经系统疾病的重要手段。尽管利用人工智能的独特能力来实现响应式神经刺激具有巨大潜力，但实时（低延迟）处理、低功耗和散热约束等限制因素使其成为极具挑战性的命题。使用基于人工智能的复杂模型进行个性化神经刺激依赖于将数据向后遥测至外部系统（例如基于云的医疗中介系统和生态系统）。虽然这可以成为一种解决方案，但在植入式神经调控设备中集成持续学习功能（如癫痫发作预测等应用）仍是一个未解难题。我们认为，神经形态架构在实现复杂的片上神经信号分析和人工智能驱动的个性化治疗方面具有卓越潜力。通过将数据处理和特征提取所需的总数据量减少三个数量级以上，神经形态计算在硬件-固件协同设计中的高能效和高存储效率，可被视为解决资源受限型植入式神经调控系统的可行方案。本文提出"神经形态神经调控"这一全新概念——一种新型闭环响应式反馈系统，并重点阐述了其彻底改变用于患者特异性治疗的植入式脑机微系统的潜力。