# Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines

arXiv: https://arxiv.org/abs/2205.06313 · HTML: https://mlsanity.com/paper/2205.06313/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 80 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 4 of 4)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Reachability in Vector Addition Systems is Primitive-Recursive in Fixed Dimension](https://mlsanity.com/paper/1903.08575/) (2019)
- [A reaction network scheme which implements the EM algorithm](https://mlsanity.com/paper/1804.09062/) (2018)
- [Decision Making Using Probabilistic Inference Methods](https://mlsanity.com/paper/1303.5428/) (2013)

## Built on by (most cited first, 2 of 2)

- [Equilibrium Propagation: the Quantum and the Thermal Cases](https://mlsanity.com/paper/2405.08467/) (2024)
- [Autonomous Learning of Generative Models with Chemical Reaction Network Ensembles](https://mlsanity.com/paper/2311.00975/) (2023)

## Abstract (arXiv)

Can a micron sized sack of interacting molecules understand, and adapt to a constantly-fluctuating environment? Cellular life provides an existence proof in the affirmative, but the principles that allow for life's existence are far from being proven. One challenge in engineering and understanding biochemical computation is the intrinsic noise due to chemical fluctuations. In this paper, we draw insights from machine learning theory, chemical reaction network theory, and statistical physics to show that the broad and biologically relevant class of detailed balanced chemical reaction networks is capable of representing and conditioning complex distributions. These results illustrate how a biochemical computer can use intrinsic chemical noise to perform complex computations. Furthermore, we use our explicit physical model to derive thermodynamic costs of inference.