In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Cofré:2019:10.3390/e21090884,
author = {Cofré, R and Videla, L and Rosas, F},
doi = {10.3390/e21090884},
journal = {Entropy},
pages = {1--28},
title = {An introduction to the non-equilibrium steady states of maximum entropy spike trains},
url = {http://dx.doi.org/10.3390/e21090884},
volume = {21},
year = {2019}
}
TY - JOUR
AB - Although most biological processes are characterized by a strong temporal asymmetry, several popular mathematical models neglect this issue. Maximum entropy methods provide a principled way of addressing time irreversibility, which leverages powerful results and ideas from the literature of non-equilibrium statistical mechanics. This tutorial provides a comprehensive overview of these issues, with a focus in the case of spike train statistics. We provide a detailed account of the mathematical foundations and work out examples to illustrate the key concepts and results from non-equilibrium statistical mechanics.
AU - Cofré,R
AU - Videla,L
AU - Rosas,F
DO - 10.3390/e21090884
EP - 28
PY - 2019///
SN - 1099-4300
SP - 1
TI - An introduction to the non-equilibrium steady states of maximum entropy spike trains
T2 - Entropy
UR - http://dx.doi.org/10.3390/e21090884
UR - https://www.mdpi.com/1099-4300/21/9/884
UR - http://hdl.handle.net/10044/1/73582
VL - 21
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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