Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China.
Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China.
Nat Commun. 2020 Aug 25;11(1):4234. doi: 10.1038/s41467-020-18105-4.
Brain-machine interfaces are promising tools to restore lost motor functions and probe brain functional mechanisms. As the number of recording electrodes has been exponentially rising, the signal processing capability of brain-machine interfaces is falling behind. One of the key bottlenecks is that they adopt conventional von Neumann architecture with digital computation that is fundamentally different from the working principle of human brain. In this work, we present a memristor-based neural signal analysis system, where the bio-plausible characteristics of memristors are utilized to analyze signals in the analog domain with high efficiency. As a proof-of-concept demonstration, memristor arrays are used to implement the filtering and identification of epilepsy-related neural signals, achieving a high accuracy of 93.46%. Remarkably, our memristor-based system shows nearly 400× improvements in the power efficiency compared to state-of-the-art complementary metal-oxide-semiconductor systems. This work demonstrates the feasibility of using memristors for high-performance neural signal analysis in next-generation brain-machine interfaces.
脑机接口是一种有前途的工具，可以恢复失去的运动功能并探测大脑的功能机制。随着记录电极数量呈指数级增长，脑机接口的信号处理能力已经落后。其中一个关键的瓶颈是，它们采用的是传统的基于冯·诺依曼架构的数字计算，这与大脑的工作原理有根本的不同。在这项工作中，我们提出了一种基于忆阻器的神经信号分析系统，利用忆阻器的生物拟态特性在模拟域中高效地分析信号。作为概念验证的演示，忆阻器阵列被用于实现与癫痫相关的神经信号的滤波和识别，准确率高达 93.46%。值得注意的是，与最先进的互补金属氧化物半导体系统相比，我们基于忆阻器的系统在功率效率方面提高了近 400 倍。这项工作证明了使用忆阻器进行下一代脑机接口中的高性能神经信号分析是可行的。
