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The nonequilibrium potential today: A short review (2022)
Journal Article
Wio, H. S., Deza, J. I., Sánchez, A. D., García-García, R., Gallego, R., Revelli, J. A., & Deza, R. R. (2022). The nonequilibrium potential today: A short review. Chaos, Solitons and Fractals, 165(Part 1), 112778. https://doi.org/10.1016/j.chaos.2022.112778

A brief review is made of the birth and evolution of the “nonequilibrium potential” (NEP) concept. As if providing a landscape for qualitative reasoning were not helpful enough, the NEP adds a quantitative dimension to the qualitative theory of diffe... Read More about The nonequilibrium potential today: A short review.

qNoise: A generator of non-Gaussian colored noise (2022)
Journal Article
Deza, J. I., & Ihshaish, H. (2022). qNoise: A generator of non-Gaussian colored noise. SoftwareX, 18, Article 101034. https://doi.org/10.1016/j.softx.2022.101034

We introduce a software generator for a class of colored (self-correlated) and non-Gaussian noise, whose statistics and spectrum depend on two param- eters, q and τ. Inspired by Tsallis’ nonextensive formulation of statistical physics, the so-called... Read More about qNoise: A generator of non-Gaussian colored noise.

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.

Distinguishing the effects of internal and forced atmospheric variability in climate networks (2014)
Journal Article
Deza, J. I., Masoller, C., & Barreiro, M. (2014). Distinguishing the effects of internal and forced atmospheric variability in climate networks. Nonlinear Processes in Geophysics, 21(3), 617-631. https://doi.org/10.5194/npg-21-617-2014

The fact that the climate on the earth is a highly complex dynamical system is well-known. In the last few decades great deal of effort has been focused on understanding how climate phenomena in one geographical region affects the climate of other re... Read More about Distinguishing the effects of internal and forced atmospheric variability in climate networks.

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.