Li Zhongwu, Myers Sydney K, Xiao Jingyi, Li Yuhao, Noy Natasha, Leuski Anton, Noy Aleksandr
Materials Science Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93106, USA.
Sci Adv. 2025 Jul 25;11(30):eadv6603. doi: 10.1126/sciadv.adv6603. Epub 2025 Jul 23.
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 - 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.
具有记忆能力的离子器件能够模拟神经功能，实现神经形态计算和生物医学应用。在本研究中，我们报道了一种基于水滴界面双层组装的离子脉冲突触。在逐步三角电压下，该器件呈现出耦合的忆容 - 忆阻行为，表现出不交叉的收缩迟滞回线。这种迟滞离子动力学可以通过修饰双层组件、重组蛋白质通道或调整液滴组装构型来调控。水滴界面突触（DIS）在经典条件下表现出诸如双脉冲易化/抑制、脉冲率依赖性可塑性、赫布学习和短期联想学习等基本神经形态行为。我们还使用带有DIS的储层计算来实现两种学习算法：一种识别手写数字的分类算法和一种学习玩井字棋棋盘游戏的强化学习算法。
