Multichannel parallel processing of neural signals in memristor arrays

作者：Zhengwu Liu, Jianshi Tang, Bin Gao, Xinyi Li, Peng Yao, Yudeng Lin, Dingkun Liu, Bo Hong, He Qian, Huaqiang Wu · 发表于：Science Advances · 年份：2020 · DOI：10.1126/sciadv.abc4797 · 被引用次数：67 · 研究领域：Advanced Memory and Neural Computing、Neuroscience and Neural Engineering、Neural dynamics and brain function

Fully implantable neural interfaces with massive recording channels bring the gospel to patients with motor or speech function loss. As the number of recording channels rapidly increases, conventional complementary metal-oxide semiconductor (CMOS) chips for neural signal processing face severe challenges on parallelism scalability, computational cost, and power consumption. In this work, we propose a previously unexplored approach for parallel processing of multichannel neural signals in memristor arrays, taking advantage of their rich dynamic characteristics. The critical information of neural signal waveform is extracted and encoded in the memristor conductance modulation. A signal segmentation scheme is developed to adapt to device variations. To verify the fidelity of the processed results, seizure prediction is further demonstrated, with high accuracy above 95% and also more than 1000× improvement in power efficiency compared with CMOS counterparts. This work suggests that memristor arrays could be a promising multichannel signal processing module for future implantable neural interfaces.