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Chemical Boltzmann Machines

  • William Poole
  • Andrés Ortiz-Muñoz
  • Abhishek Behera
  • Nick S. Jones
  • Thomas E. Ouldridge
  • Erik Winfree
  • Manoj GopalkrishnanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10467)

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.

Notes

Acknowledgements

This work was supported in part by U.S. National Science Foundation (NSF) graduate fellowships to WP and to AOM, by NSF grant CCF-1317694 to EW, and by the Gordon and Betty Moore Foundation through Grant GBMF2809 to the Caltech Programmable Molecular Technology Initiative (PMTI), by a Royal Society University Research Fellowship to TEO, and by a Bharti Centre for Communication in IIT Bombay award to AB.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • William Poole
    • 1
  • Andrés Ortiz-Muñoz
    • 1
  • Abhishek Behera
    • 2
  • Nick S. Jones
    • 3
  • Thomas E. Ouldridge
    • 3
  • Erik Winfree
    • 1
  • Manoj Gopalkrishnan
    • 2
    Email author
  1. 1.California Institute of TechnologyPasadenaUSA
  2. 2.India Institute of Technology BombayMumbaiIndia
  3. 3.Imperial College LondonLondonUK

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