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2017 | OriginalPaper | Chapter

Chemical Boltzmann Machines

Authors : William Poole, Andrés Ortiz-Muñoz, Abhishek Behera, Nick S. Jones, Thomas E. Ouldridge, Erik Winfree, Manoj Gopalkrishnan

Published in: DNA Computing and Molecular Programming

Publisher: Springer International Publishing

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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 three 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.

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previous chapter Simplifying Analyses of Chemical Reaction Networks for Approximate Majority

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Title: Chemical Boltzmann Machines
Authors: William Poole
Andrés Ortiz-Muñoz
Abhishek Behera
Nick S. Jones
Thomas E. Ouldridge
Erik Winfree
Manoj Gopalkrishnan
Publisher: Springer International Publishing
Book: DNA Computing and Molecular Programming
Print ISBN: 978-3-319-66798-0

Electronic ISBN: 978-3-319-66799-7

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-66799-7_14

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