Spatiotemporal Integration‐Capable Dual‐Gate Dendristor for Dendritic Computation and Image Processing
Dendritic computation based on the spatiotemporal integration for excitatory and inhibitory synaptic inputs plays a pivotal role in performing sophisticated information processing. It is vital to develop artificial dendritic devices with a flexible spatiotemporal integration capacity for excitatory and inhibitory inputs driven by the inputs with the same polarity. Here, a dual-gate configuration dendristor (abbreviated as DGD) is demonstrated with p-type and n-type memtransistor characteristics based on Na<sup>+</sup>-doped MoS<sub>2</sub>. The excitatory and inhibitory synaptic responses are achieved on the DGD under the same polarity voltage stimulations. Typical dendritic computational functions, including temporal coupling, cooperation and competition, and nonlinear integration, are successfully realized. The operational mechanisms are comprehensively elaborated. Benefitting from the flexible dendritic integration capacity, the DGD exhibits remarkable image denoise and dark/light adaptation capabilities. The recognition accuracy for a noisy image with 18% noise level is increased from 64% to 93% after preprocessing. Furthermore, the DGD has retina-like dark/bright adaptation, increasing the recognition accuracies from 67% and 87% to 94% and 93% for low- and high-light images, respectively. The DGD could provide a promising candidate for next-generation edge intelligent devices capable of performing sophisticated spatiotemporal information processing tasks.
Authors
- Deen Gu (ORCID: https://orcid.org/0000-0003-4034-5620)
- Miao Shen
- Yunbo Liu (ORCID: https://orcid.org/0000-0003-2727-8515)
- Zinan Zhang (ORCID: https://orcid.org/0000-0002-1924-3374)
- Dan Cai
- Tiancheng Zhao (ORCID: https://orcid.org/0000-0002-4166-6189)
Institutions
- University of Electronic Science and Technology of China (CN)
Publication Details
- Journal
- Small
- Published
- 2025-08-29
- DOI
- https://doi.org/10.1002/smll.202503689
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00