ArticleJournal of the Royal Society, Interface2021
On reaction network implementations of neural networks.
Article in Journal of the Royal Society, Interface, 2021. 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
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Who cites it
8 citing papers in PubMed.
- Recurrent neural chemical reaction networks trained to switch dynamical behaviours through learned bifurcations.Journal of the Royal Society, Interface · 2026Article
- GenAI-Net: A generative AI framework for automated biomolecular network design.Science advances · 2026Article
- Soft Hardware, Flowing Software: Reconfigurable Microfluidics for Adaptable Chemical Computation.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- 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
- Maximum likelihood estimation of log-affine models using detailed-balanced reaction networks.Journal of mathematical biology · 2025Article
- Autonomous learning of generative models with chemical reaction network ensembles.Journal of the Royal Society, Interface · 2025Article
- Biology-inspired graph neural network encodes reactome and reveals biochemical reactions of disease.Patterns (New York, N.Y.) · 2023Article
- Programming and training rate-independent chemical reaction networks.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper is concerned with the utilization of deterministically modelled chemical reaction networks for the implementation of (feed-forward) neural networks. We develop a general mathematical framework and prove that the ordinary differential equations (ODEs) associated with certain reaction network implementations of neural networks have desirable properties including (i) existence of unique positive fixed points that are smooth in the parameters of the model (necessary for gradient descent) and (ii) fast convergence to the fixed point regardless of initial condition (necessary for efficient implementation). We do so by first making a connection between neural networks and fixed points for systems of ODEs, and then by constructing reaction networks with the correct associated set of ODEs. We demonstrate the theory by constructing a reaction network that implements a neural network with a smoothed ReLU activation function, though we also demonstrate how to generalize the construction to allow for other activation functions (each with the desirable properties listed previously). As there are multiple types of 'networks' used in this paper, we also give a careful introduction to both reaction networks and neural networks, in order to disambiguate the overlapping vocabulary in the two settings and to clearly highlight the role of each network's properties.
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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.