Ionic Potential Relaxation Effect in a Hydrogel Enabling Synapse-Like Information Processing
The next-generation brain-like intelligence based on neuromorphic architectures emphasizes learning the ionic language of the brain, aiming for efficient brain-like computation and seamless human-computer interaction. Ionic neuromorphic devices, with ions serving as information carriers, provide possibilities to achieve this goal. Soft and biocompatible ionic conductive hydrogels are an ideal substrate for constructing ionic neuromorphic devices, but it remains a challenge to modulate the ion transport behavior in hydrogels to mimic neuroelectric signals. Here, we describe an ionic potential relaxation effect in a hydrogel device prepared by sandwiching a layer of polycationic hydrogel (CH) between two layers of neutral hydrogel (NH), allowing this device to simulate various electrical signal patterns observed in biological synapses, including short- and long-term plasticity patterns. Theoretical and experimental results show that the selective permeation and hysteretic diffusion of ions caused by the anion selectivity of the CH layer are responsible for potential relaxation. Such an effect allows us with hydrogels to enable synapse-like information processing functions, including tactile perception, learning, memory, and neuromorphic computing. Additionally, the hydrogel device can operate stably even under 180° bending and 50% tensile strain, expanding the pathway for implementing advanced brain-like intelligent systems.
Authors
- Kai Xiao (ORCID: https://orcid.org/0000-0001-9829-2707)
- Youzhi Qu (ORCID: https://orcid.org/0000-0002-1056-3996)
- Jiqing Dai
- Quanying Liu (ORCID: https://orcid.org/0000-0002-2501-7656)
- Zhiyuan Liu (ORCID: https://orcid.org/0000-0001-9231-8195)
- Li Wang (ORCID: https://orcid.org/0000-0003-3202-1888)
- Hongjie Zhang (ORCID: https://orcid.org/0000-0001-5433-8611)
- Guoheng Xu (ORCID: https://orcid.org/0000-0001-6548-4737)
- Wenchao Liu (ORCID: https://orcid.org/0009-0000-7486-7254)
- Song Wang (ORCID: https://orcid.org/0000-0002-0360-1624)
- Ting Wang (ORCID: https://orcid.org/0009-0005-2119-665X)
Institutions
- University of Science and Technology of China (CN)
- Chinese Academy of Sciences (CN)
- Southern University of Science and Technology (CN)
- Moscow University «Synergy» (RU)
- Shenzhen Institutes of Advanced Technology (CN)
Publication Details
- Journal
- ACS Nano
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1021/acsnano.4c09154
- Citations
- 28
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 5.84
Funders
- Science and Technology Foundation of Shenzhen City
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Science, Technology and Innovation Commission of Shenzhen Municipality