Profile

I studied theoretical physics at the University of the Basque Country, Spain. My interests in biology brought me to the University of Oulu (Finland) where I spent my last undergraduate year studying biophysics and computation. I obtained my Ph.D. in physics at the University of Potsdam (Germany) under the supervision of Prof. Dr. Jurgen Kurths and Prof. Changsong Zhou. After visiting the Bernstein Center for Computational Neuroscience in Berlin, At present, I am a post-doctoral researcher at the Center for Brain and Cognition in the group of Prof. Gustavo Deco, working at the Human Brain Project .

Scientific Interests

I am interested in everything that physics can help understand about the nervous system, brain and cognition, but I find perticulary challenging to study the brain in a "macroscopic" manner rather than studying very localized and microscopic circuitries. If we ever want to understand such a problem as cognition, we should try to understand how information from different modalities and parts of the brain do integrate together.

My work on cortical networks has also turned my attention to the study of complex network theory. We are focused on improving current measurement definitions, detection of community / hierarchical structures and, in general, uncovering the relationship between structure and function: how does a given topology influence the function of a real networked system? How do the functional necessities and constraints of a system govern the adaptive reshape of its topological organisation?

Software

I have developed pyGAlib, a library for the analysis of graphs and complex networks using Python and NumPy. The library is easy to install, use and extend. Find a brief description here. The library and further documentation can be downloaded from its GitHub repository.

The results of our publication "Sizing complex networks" with Romain Brasselet gave rise to PathLims, a package to estimate the largest and the smallest pathlength (efficiency) of graphs and digraphs, including methods to generate extremal graphs. The package is fully compatible with pyGAlib but it was released independently. GitHub repository.

Together with Matthieu Gilson and Nikos E. Kouvaris, we have developed a novel approach to study complex networks based on the propagation of noisy perturbations. The package NetDynFlow was created to calculate the spatio-temporal flow of such perturbations and study the network properties consequently. GitHub repository.

Application of NetDynFlow for the study of functional brain connectivity based on fMRI activity often requires whole-brain Effective Connectivity to be estimated before. The package pyMOU does so, assuming the multivariate Ornstein-Uhlenbeck process as the generative model for the fMRI activity. GitHub repository.