Imperial College London
Alternate

Nick S Jones

Faculty of Natural Sciences, Department of Mathematics

Professor of Mathematical Sciences
 
 
 
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Contact

 

+44 (0)20 7594 1146nick.jones

 
 
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Location

 

301aSir Ernst Chain BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Poole:2017:10.1007/978-3-319-66799-7_14,
author = {Poole, W and Ortiz-Muñoz, A and Behera, A and Jones, NS and Ouldridge, TE and Winfree, E and Gopalkrishnan, M},
doi = {10.1007/978-3-319-66799-7_14},
journal = {Lecture Notes in Computer Science},
pages = {210--231},
title = {Chemical Boltzmann Machines},
url = {http://dx.doi.org/10.1007/978-3-319-66799-7_14},
volume = {10467},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - How smart can a micron-sized bag of chemicals be? How can an artificial orreal cell make inferences about its environment? From which kinds ofprobability distributions can chemical reaction networks sample? We begintackling these questions by showing four ways in which a stochastic chemicalreaction network can implement a Boltzmann machine, a stochastic neural networkmodel that can generate a wide range of probability distributions and computeconditional probabilities. The resulting models, and the associated theorems,provide a road map for constructing chemical reaction networks that exploittheir native stochasticity as a computational resource. Finally, to show thepotential of our models, we simulate a chemical Boltzmann machine to classifyand generate MNIST digits in-silico.
AU  - Poole,W
AU  - Ortiz-Muñoz,A
AU  - Behera,A
AU  - Jones,NS
AU  - Ouldridge,TE
AU  - Winfree,E
AU  - Gopalkrishnan,M
DO  - 10.1007/978-3-319-66799-7_14
EP  - 231
PY  - 2017///
SN  - 0302-9743
SP  - 210
TI  - Chemical Boltzmann Machines
T2  - Lecture Notes in Computer Science
UR  - http://dx.doi.org/10.1007/978-3-319-66799-7_14
UR  - http://arxiv.org/abs/1707.06221v1
UR  - http://hdl.handle.net/10044/1/53171
VL  - 10467
ER  -
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