Replicating brain-like computation with fluidic memristors offers advantages in energy efficiency and chemical responsiveness over solid-state devices, yet scaling remains challenging due to complex fabrication and their amorphous nature. Herein, we developed a confined hydrogel fluidic memristor by forming a gel-gel interface at the micropore orifice. This design with confined hydrogel enables scalable fabrication of a 10×10 fluidic memristor array (FMA) on polyimide micropores. FMA exhibits fundamental neuromorphic behaviors like paired-pulse facilitation/depression, spike-rate-dependent plasticity, and chemical-regulated plasticity. We also used reservoir computing algorithms with FMA to recognize both computer-generated black-and-white digit images and handwritten digits, achieving a classification accuracy of 89.5% on the Modified National Institute of Standards and Technology dataset. This study demonstrates a hydrogel confined fluidic memristor array, paving an avenue for creating large-scale fluidic memristor arrays and hardware intelligence with ions. Fluidic memristor arrays share a promising similarity with biological neural systems, yet their scalability and integration present significant hurdles. Here, Guo et al. report a 10×10 hydrogel-based fluidic memristor array capable of implementing fundamental neuroplasticity and reservoir computing.
Guo et al. (2026) studied this question.