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A nonequilibrium-potential approach to competition in neural populations (2019)
Journal Article
Deza, R. R., Deza, I., Martínez, N., Mejías, J. F., & Wio, H. S. (2019). A nonequilibrium-potential approach to competition in neural populations. Frontiers in Physics, 6(154), https://doi.org/10.3389/fphy.2018.00154

Energy landscapes are a highly useful aid for the understanding of dynamical systems, and a particularly valuable tool for their analysis. For a broad class of rate neural- network models of relevance in neuroscience, we derive a global Lyapunov func... Read More about A nonequilibrium-potential approach to competition in neural populations.

Assessing the direction of climate interactions by means of complex networks and information theoretic tools (2015)
Journal Article
Deza, J. I., Barreiro, M., & Masoller, C. (2015). Assessing the direction of climate interactions by means of complex networks and information theoretic tools. Chaos, 25(3), https://doi.org/10.1063/1.4914101

© 2015 AIP Publishing LLC. An estimate of the net direction of climate interactions in different geographical regions is made by constructing a directed climate network from a regular latitude-longitude grid of nodes, using a directionality index (DI... Read More about Assessing the direction of climate interactions by means of complex networks and information theoretic tools.

Inferring interdependencies in climate networks constructed at inter-annual, intra-season and longer time scales (2013)
Journal Article
Deza, J. I., Barreiro, M., & Masoller, C. (2013). Inferring interdependencies in climate networks constructed at inter-annual, intra-season and longer time scales. European Physical Journal - Special Topics, 222(2), 511-523. https://doi.org/10.1140/epjst/e2013-01856-5

We study global climate networks constructed by means of ordinal time series analysis. Climate interdependencies among the nodes are quantified by the mutual information, computed from time series of monthly-averaged surface air temperature anomalies... Read More about Inferring interdependencies in climate networks constructed at inter-annual, intra-season and longer time scales.