Affective computing for human-machine interaction via a bionic organic memristor exhibiting selective in situ activation.
Bingjie GuoXiaolong ZhongZhe YuZhilong HeShuzhi LiuZhixin WuSixian LiuYanbo GuoWeilin ChenHongxiao DuanJianmin ZengPingqi GaoBin ZhangQian ChenHaidong HeYu ChenGang LiuPublished in: Materials horizons (2024)
Affective computing, representing the forefront of human-machine interaction, is confronted with the pressing challenges of the execution speed and power consumption brought by the transmission of massive data. Herein, we introduce a bionic organic memristor inspired by the ligand-gated ion channels (LGICs) to facilitate near-sensor affective computing based on electroencephalography (EEG). It is constructed from a coordination polymer comprising Co ions and benzothiadiazole (Co-BTA), featuring multiple switching sites for redox reactions. Through advanced characterizations and theoretical calculations, we demonstrate that when subjected to a bias voltage, only the site where Co ions bind with N atoms from four BTA molecules becomes activated, while others remain inert. This remarkable phenomenon resembles the selective in situ activation of LGICs on the postsynaptic membrane for neural signal regulation. Consequently, the bionic organic memristor network exhibits outstanding reliability (200 000 cycles), exceptional integration level (2 10 pixels), ultra-low energy consumption (4.05 pJ), and fast switching speed (94 ns). Moreover, the built near-sensor system based on it achieves emotion recognition with an accuracy exceeding 95%. This research substantively adds to the ambition of realizing empathetic interaction and presents an appealing bionic approach for the development of novel electronic devices.
Keyphrases
- endothelial cells
- water soluble
- bipolar disorder
- deep learning
- induced pluripotent stem cells
- quantum dots
- electronic health record
- depressive symptoms
- high resolution
- working memory
- machine learning
- functional connectivity
- molecular dynamics simulations
- wastewater treatment
- zika virus
- aqueous solution
- neural network