ArticleProceedings of the National Academy of Sciences of the United States of America2024
Neuromorphic one-shot learning utilizing a phase-transition material.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed.
- Instructive signals drive learning through behavioral timescale synaptic plasticity in the hippocampus and beyond.Current research in neurobiology · 2026Article
- Cellular and subcellular specialization enables biology-constrained deep learning.Cell reports · 2026Article
- Behavioral timescale synaptic plasticity: properties, elements and functions.Nature neuroscience · 2026Review
- NeoHebbian synapses to accelerate online training of neuromorphic hardware.Scientific reports · 2026Article
- Behavioral Timescale Synaptic Plasticity: A Burst in the Field of Learning and Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Review
- Minimal motifs for habituating systems.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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
Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO
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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.