Farronato Matteo, Mannocci Piergiulio, Milozzi Alessandro, Compagnoni Christian Monzio, Barcellona Alessandro, Arena Andrea, Crepaldi Marco, Panuccio Gabriella, Ielmini Daniele
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
Electronic Design Laboratory, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152 Genova, Italy.
Sci Adv. 2025 Jan 17;11(3):eadr3241. doi: 10.1126/sciadv.adr3241.
Neurological disorders are a substantial global health burden, affecting millions of people worldwide. A key challenge in developing effective treatments and preventive measures is the realization of low-power wearable systems with early detection capabilities. Traditional strategies rely on machine learning algorithms, but their computational demands often exceed what miniaturized systems can provide. Neuromorphic computing, inspired by the human brain, demonstrated capabilities of on-chip computing with low power consumption. In this context, bidimensional (2D) semiconductors hold notable promise, thanks to their unique electronic properties, atomic-scale thickness, and scalability, making them ideal for low-power applications. This work presents a neuromorphic reservoir computing system exploiting MoS-based charge trap memories (CTMs) for processing of electrophysiological signals. Real-time seizures detection is achieved, thanks to the nonlinear integration of local-field potential (LFP) recorded from in vitro rodent models of ictogenesis. The results support MoS-based CTMs for low-power biomedical devices in clinical diagnosis and treatment of epilepsy.
神经疾病是一项重大的全球健康负担，影响着全球数百万人。开发有效治疗方法和预防措施的一个关键挑战是实现具有早期检测能力的低功耗可穿戴系统。传统策略依赖机器学习算法，但其计算需求往往超出小型化系统所能提供的范围。受人类大脑启发的神经形态计算展示了低功耗片上计算的能力。在这种背景下，二维（2D）半导体因其独特的电子特性、原子级厚度和可扩展性而具有显著前景，使其成为低功耗应用的理想选择。这项工作提出了一种利用基于MoS的电荷俘获存储器（CTM）来处理电生理信号的神经形态储层计算系统。得益于从体外癫痫发生啮齿动物模型记录的局部场电位（LFP）的非线性积分，实现了实时癫痫发作检测。这些结果支持基于MoS的CTM用于癫痫临床诊断和治疗中的低功耗生物医学设备。
