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Chemical Boltzmann Machines

  • 2017
  • OriginalPaper
  • Chapter
Published in:
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.
W. Poole, A. Ortiz-Muñoz and A. Behera—Contributed Equally.

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previous chapter Simplifying Analyses of Chemical Reaction Networks for Approximate Majority
next chapter A General-Purpose CRN-to-DSD Compiler with Formal Verification, Optimization, and Simulation Capabilities
Title
Chemical Boltzmann Machines
Authors
William Poole
Andrés Ortiz-Muñoz
Abhishek Behera
Nick S. Jones
Thomas E. Ouldridge
Erik Winfree
Manoj Gopalkrishnan
Copyright Year
2017
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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