ArticleNature communications2022
Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Memristors for Bayesian in-memory computing.Nature materials · 2026Article
- Coupled dual-channel memristors for hardware-native trustworthy Bayesian intelligence.Nature communications · 2026Article
- Bayesian continual learning and forgetting in neural networks.Nature communications · 2025Article
- Perovskite Neuromorphic Engine for Transformer Architectures.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Ferroelectric NAND for efficient hardware bayesian neural networks.Nature communications · 2025Article
- Photonic Bayesian Neural Networks: Leveraging Programmable Noise for Robust and Uncertainty-Aware Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Superior probabilistic computing using operationally stable probabilistic-bit constructed by a manganite nanowire.National science review · 2025Article
- Advances in Metal Halide Perovskite Memristors: A Review from a Co-Design Perspective.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials.Nano-micro letters · 2024Review
- Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks.Nature communications · 2023Article
- A bio-inspired visuotactile neuron for multisensory integration.Nature communications · 2023Article
- Review
- Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network.Sensors (Basel, Switzerland) · 2023Review
- Spintronic leaky-integrate-fire spiking neurons with self-reset and winner-takes-all for neuromorphic computing.Nature communications · 2023Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial neural networks have demonstrated superiority over traditional computing architectures in tasks such as pattern classification and learning. However, they do not measure uncertainty in predictions, and hence they can make wrong predictions with high confidence, which can be detrimental for many mission-critical applications. In contrast, Bayesian neural networks (BNNs) naturally include such uncertainty in their model, as the weights are represented by probability distributions (e.g. Gaussian distribution). Here we introduce three-terminal memtransistors based on two-dimensional (2D) materials, which can emulate both probabilistic synapses as well as reconfigurable neurons. The cycle-to-cycle variation in the programming of the 2D memtransistor is exploited to achieve Gaussian random number generator-based synapses, whereas 2D memtransistor based integrated circuits are used to obtain neurons with hyperbolic tangent and sigmoid activation functions. Finally, memtransistor-based synapses and neurons are combined in a crossbar array architecture to realize a BNN accelerator for a data classification task.
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