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Chemical Boltzmann Machines

Poole, William and Ortiz-Muñoz, Andrés and Behera, Abhishek and Jones, Nick S. and Ouldridge, Thomas E. and Winfree, Erik and Gopalkrishnan, Manoj (2017) Chemical Boltzmann Machines. In: DNA Computing and Molecular Programming. Lecture Notes in Computer Science. No.10467. Springer , Cham, Switzerland, pp. 210-231. ISBN 978-3-319-66798-0.

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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.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Ortiz-Muñoz, Andrés0000-0003-1824-3230
Winfree, Erik0000-0002-5899-7523
Additional Information:© Springer International Publishing AG 2017. First Online: 24 August 2017. 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.
Funding AgencyGrant Number
NSF Graduate Research FellowshipUNSPECIFIED
Gordon and Betty Moore FoundationGBMF2809
India Institute of TechnologyUNSPECIFIED
Subject Keywords:DNA origami; Knot theory; Graph theory; Chinese postman problem
Series Name:Lecture Notes in Computer Science
Issue or Number:10467
Record Number:CaltechAUTHORS:20181126-090207704
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:91153
Deposited By: Tony Diaz
Deposited On:26 Nov 2018 17:38
Last Modified:16 Nov 2021 03:38

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