Lv Honglin, Liu Rui, Zhang Yin
Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast University, Nanjing 211189, China.
ACS Sens. 2025 Jul 25;10(7):4804-4824. doi: 10.1021/acssensors.5c01063. Epub 2025 Jun 30.
Biological brains that use ions as signaling carriers exhibit powerful computing and learning capabilities. Inspired by this, ion-based neuromorphic devices have gained widespread attention. As a neuromorphic device, the fluidic ionic memristor can simulate not only synaptic plasticity but also the transduction of chemical-electrical signals because of unique ions biocompatibility. Furthermore, ionic circuits provide a versatile platform for constructing neural networks, demonstrating significant potential for simulating biologically inspired computations. However, ion-based neuromorphic computational circuits are still in their infancy, and there is an urgent need for comprehensive guidance on their development. In this perspective, we first describe the development of ionic devices and the working principle of fluidic ionic memristors as well as their application in the simulation of biological synapses systematically. We then summarize the construction of neuromorphic computing integrated circuits in a fluidic environment. Finally, a series of solutions to the challenges faced by ionic devices involving performance indexes and the design of neuromorphic circuits are also discussed.
使用离子作为信号载体的生物大脑展现出强大的计算和学习能力。受此启发，基于离子的神经形态器件受到了广泛关注。作为一种神经形态器件，流体离子忆阻器由于独特的离子生物相容性，不仅可以模拟突触可塑性，还能模拟化学 - 电信号的转换。此外，离子电路为构建神经网络提供了一个通用平台，在模拟生物启发计算方面显示出巨大潜力。然而，基于离子的神经形态计算电路仍处于起步阶段，迫切需要对其发展进行全面指导。从这个角度出发，我们首先系统地描述了离子器件的发展、流体离子忆阻器的工作原理及其在生物突触模拟中的应用。然后我们总结了在流体环境中神经形态计算集成电路的构建。最后，还讨论了离子器件在性能指标和神经形态电路设计方面所面临挑战的一系列解决方案。
