DOE OSTI · 2589375
Neuromorphic ionic computing in droplet interface synapses
Abstract
Ionic devices with memory capabilities can emulate neural functionality, enabling neuromorphic computing and biomedical applications. In this study, we report an ionic spiking synapse based on aqueous droplet interface bilayer assembly. Under stepwise triangular voltages, the device displays coupled memcapacitive-memristive behavior, showing noncrossing pinched hysteretic I-V loops. This hysteretic ion dynamics can be regulated by modifying bilayer components, reconstituting protein channels, or adjusting droplet assembly configuration. Droplet interface synapses (DIS) exhibit fundamental neuromorphic behaviors such as paired-pulse facilitation/depression, spike rate–dependent plasticity, Hebbian learning, and short-term associative learning under classical conditioning. We also used reservoir computing with DIS to implement two learning algorithms: a classification algorithm that recognizes handwritten digits and a reinforcement learning algorithm that learns to play a board game of tic-tac-toe.
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Li, Zhongwu [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0000000188256307), Myers, Sydney K. [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0000000280237475), Xiao, Jingyi [University of California, Santa Barbara, CA (United States)] (ORCID:0000000183286363), Li, Yuhao [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0000000318390900), Noy, Natasha [Google Research, Mountain View, CA (United States)] (ORCID:0000000274370624), Leuski, Anton [University of Southern California, Los Angeles, CA (United States)] (ORCID:0000000159875686), Noy, Aleksandr [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); University of California, Merced, CA (United States)] (ORCID:0000000349242652). 2025-07-23. Neuromorphic ionic computing in droplet interface synapses. https://doi.org/10.1126/sciadv.adv6603
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