Bioinspired ion-shuttling memristor with both neuromorphic functions and ion selectivity
The fluidic memristor has attracted growing attention as a promising candidate for neuromorphic computing and brain-computer interfaces. However, a fluidic memristor with ion selectivity as that of natural ion channels remains a key challenge. Herein, inspired by the structure of natural biomembranes, we developed an ion-shuttling memristor (ISM) by utilizing organic solvents and artificial carriers to emulate ion channels embedded in biomembranes, which exhibited both neuromorphic functions and ion selectivity. Pinched hysteresis <i>I-V</i> loop curve, scan rate dependency, and distinctive impedance spectra confirmed the memristive characteristics of the as-prepared device. Moreover, the memory mechanism was discussed theoretically and validated by finite-element modeling. The ISM features multiple neuromorphic functions, such as paired-pulse facilitation, paired-pulse depression, and learning-experience behavior. More importantly, the ion selectivity of the ISM was observed, which allowed further emulation of ion-selective neural functions like resting membrane potential. Benefiting from the structural similarity to membrane-embedded ion channels, the ISM opens the door for ion-based neuromorphic computing and sophisticated chemical regulation by manipulating multifarious ions with neuromorphic functions.
Authors
- Cong Pan (ORCID: https://orcid.org/0000-0001-5959-4294)
- Boyang Xie
- Wenjie Ma (ORCID: https://orcid.org/0000-0003-1268-4959)
- Tianyi Xiong (ORCID: https://orcid.org/0000-0002-2076-7730)
- Guang-Can Guo
- Ping Yu (ORCID: https://orcid.org/0000-0002-6096-1933)
Institutions
- Chinese Academy of Sciences (CN)
- Beijing National Laboratory for Molecular Sciences (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Proceedings of the National Academy of Sciences
- Published
- 2025-03-05
- DOI
- https://doi.org/10.1073/pnas.2417040122
- Citations
- 15
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 10.96
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China