Imperial College London
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DrThomasOuldridge

Faculty of Engineering, Department of Bioengineering

Reader in Biomolecular Systems
 
 
 
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Contact

 

t.ouldridge Website CV

 
 
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Location

 

4.04Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Brittain:2017:1742-5468/aa71d4,
author = {Brittain, RA and Jones, NS and Ouldridge, TE},
doi = {1742-5468/aa71d4},
journal = {Journal of Statistical Mechanics-Theory and Experiment},
title = {What we learn from the learning rate},
url = {http://dx.doi.org/10.1088/1742-5468/aa71d4},
volume = {2017},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The learning rate is an information-theoretical quantity for bipartite Markovchains describing two coupled subsystems. It is defined as the rate at whichtransitions in the downstream subsystem tend to increase the mutual informationbetween the two subsystems, and is bounded by the dissipation arising fromthese transitions. Its physical interpretation, however, is unclear, althoughit has been used as a metric for the sensing performance of the downstreamsubsystem. In this paper we explore the behaviour of the learning rate for anumber of simple model systems, establishing when and how its behaviour isdistinct from the instantaneous mutual information between subsystems. In thesimplest case, the two are almost equivalent. In more complex steady-statesystems, the mutual information and the learning rate behave qualitativelydistinctly, with the learning rate clearly now reflecting the rate at which thedownstream system must update its information in response to changes in theupstream system. It is not clear whether this quantity is the most naturalmeasure for sensor performance, and, indeed, we provide an example in whichoptimising the learning rate over a region of parameter space of the downstreamsystem yields an apparently sub-optimal sensor.
AU  - Brittain,RA
AU  - Jones,NS
AU  - Ouldridge,TE
DO  - 1742-5468/aa71d4
PY  - 2017///
SN  - 1742-5468
TI  - What we learn from the learning rate
T2  - Journal of Statistical Mechanics-Theory and Experiment
UR  - http://dx.doi.org/10.1088/1742-5468/aa71d4
UR  - http://arxiv.org/abs/1702.06041v1
UR  - http://hdl.handle.net/10044/1/49579
VL  - 2017
ER  -
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