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arXiv · 1707.06221

Chemical Boltzmann Machines

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

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William Poole, Andrés Ortiz-Muñoz, Abhishek Behera, Nick S. Jones, Thomas E. Ouldridge, Erik Winfree, Manoj Gopalkrishnan. 2017-07-19. Chemical Boltzmann Machines. https://doi.org/10.1007/978-3-319-66799-7_14

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