Chemical Boltzmann Machines
Abstract
How smart can a micron-sized bag of chemicals be? How can an artificial or real cell make inferences about its environment? From which kinds of probability distributions can chemical reaction networks sample? We begin tackling these questions by showing four ways in which a stochastic chemical reaction network can implement a Boltzmann machine, a stochastic neural network model that can generate a wide range of probability distributions and compute conditional probabilities. The resulting models, and the associated theorems, provide a road map for constructing chemical reaction networks that exploit their native stochasticity as a computational resource. Finally, to show the potential of our models, we simulate a chemical Boltzmann machine to classify and generate MNIST digits in-silico.
Cite
@article{arxiv.1707.06221,
title = {Chemical Boltzmann Machines},
author = {William Poole and Andrés Ortiz-Muñoz and Abhishek Behera and Nick S. Jones and Thomas E. Ouldridge and Erik Winfree and Manoj Gopalkrishnan},
journal= {arXiv preprint arXiv:1707.06221},
year = {2020}
}
Comments
To Appear at DNA 23 Conference