ArticleProceedings of the National Academy of Sciences of the United States of America2024
Machine learning without a processor: Emergent learning in a nonlinear analog network.
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 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
8 citing papers in PubMed.
- Collective behavior and memory states in flow networks with tunable bistability.Nature communications · 2026Article
- Nonlinear thermodynamic computing out of equilibrium.Nature communications · 2026Article
- Trainable computation in molecular networks.bioRxiv : the preprint server for biology · 2025Article
- Training and retraining liquid crystal elastomer metamaterials for pluripotent functionality.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Temporal Contrastive Learning through implicit non-equilibrium memory.Nature communications · 2025Article
- A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Training neural networks using physical equations of motion.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Machine learning without a processor: Emergent learning in a nonlinear analog network.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic
Indexed as
Identifiers
What OpenQuestion holds
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.