ArticleNature communications2024
Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Ferroelectrically programmable lanthanide luminescent memristor.Nature communications · 2026Article
- A Single OLED With Hybrid Synaptic Plasticity Enabling In Situ Neuromorphic Processing and Visualization.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Amphibian-inspired neuromorphic dynamic vision systems based on ferroelectric field-effect transistor.Nature communications · 2026Article
- Electroluminescent perovskite QD-based neural networks for energy-efficient and accelerate multitasking learning.Science advances · 2026Article
- Devices, Functions, and Applications of Artificial Neuromorphic Visual Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Multifunctional Colloidal Quantum Dots-Based Light-Emitting Devices for On-Chip Integration.Nanomaterials (Basel, Switzerland) · 2025Review
- Electrically programmable organic in-display neuromorphic computing.National science review · 2025Article
- Responsive Molecules for Organic Neuromorphic Devices: Harnessing Memory Diversification.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Memristor-Based Artificial Neural Networks for Hardware Neuromorphic Computing.Research (Washington, D.C.) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Deep neural networks have revolutionized several domains, including autonomous driving, cancer detection, and drug design, and are the foundation for massive artificial intelligence models. However, hardware neural network reports still mainly focus on shallow networks (2 to 5 layers). Implementing deep neural networks in hardware is challenging due to the layer-by-layer structure, resulting in long training times, signal interference, and low accuracy due to gradient explosion/vanishing. Here, we utilize negative ultraviolet photoconductive light-emitting memristors with intrinsic parallelism and hardware-software co-design to achieve electrical information's optical cross-layer transmission. We propose a hybrid ultra-deep photoelectric neural network and an ultra-deep super-resolution reconstruction neural network using light-emitting memristors and cross-layer block, expanding the networks to 54 and 135 layers, respectively. Further, two networks enable transfer learning, approaching or surpassing software-designed networks in multi-dataset recognition and high-resolution restoration tasks. These proposed strategies show great potential for high-precision multifunctional hardware neural networks and edge artificial intelligence.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.