RT Journal Article SR Electronic T1 Multichannel parallel processing of neural signals in memristor arrays JF Science Advances JO Sci Adv FD American Association for the Advancement of Science SP eabc4797 DO 10.1126/sciadv.abc4797 VO 6 IS 41 A1 Liu, Zhengwu A1 Tang, Jianshi A1 Gao, Bin A1 Li, Xinyi A1 Yao, Peng A1 Lin, Yudeng A1 Liu, Dingkun A1 Hong, Bo A1 Qian, He A1 Wu, Huaqiang YR 2020 UL http://advances.sciencemag.org/content/6/41/eabc4797.abstract AB 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.