"Discovering is an emotion, a path illuminated by feelings that leads to rational demonstrations."

## Gorka Zamora-López, *Ph.D.*

*Post-Doctoral Research Fellow*

Computational Neuroscience Group

Center for Brain and Cognition

Universitat Pompeu Fabra - Barcelona

### 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.