ArticleNature communications2020
Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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.
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Who cites it
18 citing papers in PubMed.
- An On-Demand Neuromorphic Vision System Enabled by a Multi-Paradigm Neuromorphic Device and Hierarchical Reconfigurability Designed from Device to System Level.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Bio-inspired cross-modal super-additive plasticity for seamless visual processing-in-sensory and -in-memory.Nature communications · 2025Article
- High-order dynamics in an ultra-adaptive neuromorphic vision device.Nature nanotechnology · 2025Article
- Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing.Light, science & applications · 2025Article
- Optogenetic neuromuscular actuation of a miniature electronic biohybrid robot.Science robotics · 2025Article
- Perovskite Neuromorphic Engine for Transformer Architectures.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Single organic electrochemical neuron capable of anticoincidence detection.Science advances · 2025Article
- AI-based prediction of androgen receptor expression and its prognostic significance in prostate cancer.Scientific reports · 2025Article
- Article
- Miniaturized spectrometer with intrinsic long-term image memory.Nature communications · 2024Article
- Integrated In-Memory Sensor and Computing of Artificial Vision Based on Full-vdW Optoelectronic Ferroelectric Field-Effect Transistor.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Highly-scaled and fully-integrated 3-dimensional ferroelectric transistor array for hardware implementation of neural networks.Nature communications · 2023Article
- Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity.Science advances · 2022Article
- Optical synaptic devices with ultra-low power consumption for neuromorphic computing.Light, science & applications · 2022Article
- Ultralow-Power Machine Vision with Self-Powered Sensor Reservoir.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- CMOS-compatible compute-in-memory accelerators based on integrated ferroelectric synaptic arrays for convolution neural networks.Science advances · 2022Article
- Review of deep learning: concepts, CNN architectures, challenges, applications, future directions.Journal of big data · 2021Article
- Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing.iScience · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Shallow feed-forward networks are incapable of addressing complex tasks such as natural language processing that require learning of temporal signals. To address these requirements, we need deep neuromorphic architectures with recurrent connections such as deep recurrent neural networks. However, the training of such networks demand very high precision of weights, excellent conductance linearity and low write-noise- not satisfied by current memristive implementations. Inspired from optogenetics, here we report a neuromorphic computing platform comprised of photo-excitable neuristors capable of in-memory computations across 980 addressable states with a high signal-to-noise ratio of 77. The large linear dynamic range, low write noise and selective excitability allows high fidelity opto-electronic transfer of weights with a two-shot write scheme, while electrical in-memory inference provides energy efficiency. This method enables implementing a memristive deep recurrent neural network with twelve trainable layers with more than a million parameters to recognize spoken commands with >90% accuracy.
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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.