ArticleJournal of the Royal Society, Interface2025
Autonomous learning of generative models with chemical reaction network ensembles.
Article in Journal of the Royal Society, Interface, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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Who cites it
4 citing papers in PubMed.
- Trainable computation in molecular networks.bioRxiv : the preprint server for biology · 2025Article
- Modelling the prebiotic origins of regulation and agency in evolving protocell ecologies.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Temporal Contrastive Learning through implicit non-equilibrium memory.Nature communications · 2025Article
- Autonomous learning of generative models with chemical reaction network ensembles.Journal of the Royal Society, Interface · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Can a micron-sized sack of interacting molecules autonomously learn an internal model of a complex and fluctuating environment? We draw insights from control theory, machine learning theory, chemical reaction network theory and statistical physics to develop a general architecture whereby a broad class of chemical systems can autonomously learn complex distributions. Our construction takes the form of a chemical implementation of machine learning's optimization workhorse: gradient descent on the relative entropy cost function, which we demonstrate can be viewed as a form of integral feedback control. We show how this method can be applied to optimize any detailed balanced chemical reaction network and that the construction is capable of using hidden units to learn complex distributions.
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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.