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Contrastive Hebbian Learning for Multicomponent Liquids

Authors: Yancheng Du, Cameron Chalk, Salvador Buse, Lulu Qian, and Erik Winfree

Published in: LIPIcs, Volume 387, 32nd International Conference on DNA Computing and Molecular Programming (DNA 32) (2026)

Abstract

Molecules with designed interactions can serve as substrates for information processing; demonstrated examples include algorithmic self-assembly of DNA tiles and DNA strand displacement reactions. These two well-established paradigms correspond to solid-phase-like behavior, where spatially structured molecular assemblies grow by crystalline attachment, and gas-phase-like behavior, where a dilute mixture of freely diffusing molecules is governed by mass-action kinetics of reactions. A third paradigm, liquid-liquid phase separation, is a fundamental phenomenon in physical and biological systems, giving rise to membraneless compartments that facilitate complex information processing. Recent studies have shown that multicomponent liquid mixtures described by lattice models are analogous to Boltzmann machines and can implement neural computation. Mean-field models have also suggested a connection to Hopfield networks and illustrated complex decision-making during condensation from a reservoir. As an alternative to previous approaches to train liquid interaction energies using backpropagation through a loss function, here we derive "local" learning rules based on contrastive Hebbian learning that may be more biologically and physically plausible. The trained systems demonstrate a range of computational tasks, including associative recall, linear and nonlinear input-output relations, pattern classification, and spatial phase separation. Our work suggests that the computational potential of liquid-phase molecular systems could be unlocked in real physical systems with local learning rules.

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Yancheng Du, Cameron Chalk, Salvador Buse, Lulu Qian, and Erik Winfree. Contrastive Hebbian Learning for Multicomponent Liquids. In 32nd International Conference on DNA Computing and Molecular Programming (DNA 32). Leibniz International Proceedings in Informatics (LIPIcs), Volume 387, pp. 13:1-13:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{du_et_al:LIPIcs.DNA.32.13,
  author =	{Du, Yancheng and Chalk, Cameron and Buse, Salvador and Qian, Lulu and Winfree, Erik},
  title =	{{Contrastive Hebbian Learning for Multicomponent Liquids}},
  booktitle =	{32nd International Conference on DNA Computing and Molecular Programming (DNA 32)},
  pages =	{13:1--13:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-444-4},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{387},
  editor =	{Scalise, Dominic and Schweller, Robert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DNA.32.13},
  URN =		{urn:nbn:de:0030-drops-267838},
  doi =		{10.4230/LIPIcs.DNA.32.13},
  annote =	{Keywords: multicomponent liquid, liquid-liquid phase separation, continuous Hopfield network, contrastive Hebbian learning}
}

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DOI: 10.4230/LIPIcs.DNA.2020.2

Simplifying Chemical Reaction Network Implementations with Two-Stranded DNA Building Blocks

Authors: Robert F. Johnson and Lulu Qian

Published in: LIPIcs, Volume 174, 26th International Conference on DNA Computing and Molecular Programming (DNA 26) (2020)

Abstract

In molecular programming, the Chemical Reaction Network model is often used to describe real or hypothetical systems. Often, an interesting computational task can be done with a known hypothetical Chemical Reaction Network, but often such networks have no known physical implementation. One of the important breakthroughs in the field was that any Chemical Reaction Network can be physically implemented, approximately, using DNA strand displacement mechanisms. This allows us to treat the Chemical Reaction Network model as a programming language and the implementation schemes as its compiler. This also suggests that it would be useful to optimize the result of such a compilation, and in general to find effective ways to design better DNA strand displacement systems. We discuss DNA strand displacement systems in terms of "motifs", short sequences of elementary DNA strand displacement reactions. We argue that describing such motifs in terms of their inputs and outputs, then building larger systems out of the abstracted motifs, can be an efficient way of designing DNA strand displacement systems. We discuss four previously studied motifs in this abstracted way, and present a new motif based on cooperative 4-way strand exchange. We then show how Chemical Reaction Network implementations can be built out of abstracted motifs, discussing existing implementations as well as presenting two new implementations based on 4-way strand exchange, one of which uses the new cooperative motif. The new implementations both have two desirable properties not found in existing implementations, namely both use only at most 2-stranded DNA complexes for signal and fuel complexes and both are physically reversible. There are reasons to believe that those properties may make them more robust and energy-efficient, but at the expense of using more fuel complexes than existing implementation schemes.

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Robert F. Johnson and Lulu Qian. Simplifying Chemical Reaction Network Implementations with Two-Stranded DNA Building Blocks. In 26th International Conference on DNA Computing and Molecular Programming (DNA 26). Leibniz International Proceedings in Informatics (LIPIcs), Volume 174, pp. 2:1-2:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{johnson_et_al:LIPIcs.DNA.2020.2,
  author =	{Johnson, Robert F. and Qian, Lulu},
  title =	{{Simplifying Chemical Reaction Network Implementations with Two-Stranded DNA Building Blocks}},
  booktitle =	{26th International Conference on DNA Computing and Molecular Programming (DNA 26)},
  pages =	{2:1--2:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-163-4},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{174},
  editor =	{Geary, Cody and Patitz, Matthew J.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DNA.2020.2},
  URN =		{urn:nbn:de:0030-drops-129557},
  doi =		{10.4230/LIPIcs.DNA.2020.2},
  annote =	{Keywords: Molecular programming, DNA computing, Chemical Reaction Networks, DNA strand displacement}
}

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Documents authored by Qian, Lulu

Contrastive Hebbian Learning for Multicomponent Liquids

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Simplifying Chemical Reaction Network Implementations with Two-Stranded DNA Building Blocks

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