SOP 2018

Self-organized patterns on complex networks and the brain

26 September 2018

A satellite workshop of the
2018 Conference on Complex Systems



Dynamical processes on complex networks have generated widespread interest in recent years because of their interdisciplinary applications to a large plethora of problems, ranging from physics and chemistry to biology and social science. Macroscopic self-organized structures can spontaneously emerge from a set of microscopic interacting constituents, an intrinsic ability of the examined systems that proves particularly attractive when the dynamics takes place on a complex network. The topology of the hosting networks plays a crucial role in seeding and shaping collective patterns, an observation, which is nowadays accepted as a paradigm in the realm of complex systems. While synchronization problems on networks have a long history, more "exotic patterns", such as Turing patterns, bump states and chimera states have been addressed only recently for dynamical systems on graphs. A rapidly developing field of special interest for dynamical networks is network neuroscience. Modern network neuroscience treats the brain as a complex network that supports --among other functions-- the processing and integration of information. Therefore, we need to understand how the interplay between dynamics of single neurons or population of neurons and structure leads to self-organized patterns of collective behavior. For this purpose traditional dynamical models (Kuramoto, FitzHugh-Nagumo, Hopf normal form etc.) with different dynamical behavior (oscillatory, excitable, etc.) are employed on complex networks representing functional or structural connections in the brain and the emergence of self-organized patterns associated to the states of the brain is analyzed.

The main goals of the workshop are:

  1. To gather together researchers of the field in order to discuss on the currently available families of models, their respective domain of applications, the corresponding set of emergent behaviors and the possible impact of different networks topologies (simplex, multiplex, multigraphs, modular networks, hierarchical networks, etc.).
  2. To debate on the main challenges to be addressed within the field for years to come, both in theory and applications.
  3. To examine models validation with real data sets and analyze those stylized facts, which can be adequately reproduced.
  4. To foster research by exchanging knowledge on methods for abstract models targeted to the set of emergent phenomena.


Keynote speakers

  • Albert Diaz-Guilera, UBICS, Department of condensed matter physics, University of Barcelona, Spain. ()
    Remote synchronization in populations of Kuramoto oscillators

    Populations of identical Kuramoto oscillators are known to synchronize in a connected network. In a previous work we introduced a frustration parameter in a population of identical oscilaltors that produces remote synchonization in units that are not connected but are related through some type of symmetry in the network topology. Here we present new results on the application of this principle to local perturbations of the frustration parameter that induce additional patterns of synchrony in the system.
  • Meysam Hashemi Institut de Neuroscience des Systèmes - Aix-Marseille Université, Faculté de Médecine de la Timone, Marseille, France (Abstract)
    Inferring the dynamic of personalized large-scale brain network models using Bayesian framework

    Despite the importance and frequent use of Bayesian inference in brain network modelling, many challenges remain to be addressed in this context. The recent successful personalized strategies towards epilepsy treatment [1] motivated us to focus on Bayesian parameter estimation of Virtual Epileptic Patient (VEP) brain model. VEP is based on personalized brain network models derived from non-invasive structural data of individual patients. Using VEP as generative model, and the recently developed Bayesian algorithms as provided in Stan [2], our aim is to infer the dynamics of brain network model from the patient’s empirical stereotactic EEG (SEEG) data. We estimate the spatial dependence of excitability and provide a heat map capturing an estimate of epileptogenicity and our confidence thereof. The Bayesian framework taken in this work proposes an appropriate patient-specific strategy to infer epileptogenicity of the brain regions to improve outcomes after epilepsy surgery.

    [1] V.K. Jirsa, T. Proix, D. Perdikis, M.M. Woodman, H. Wang, J. Gonzalez-Martinez, C. Bernard, C. Bénar, M. Guye, P. Chauvel, F. Bartolomei, The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread, NeuroImage, VOL 145, 377-388, 2017.
    [2] The Stan Development Team, 2015. Stan: A C ++ Library for Probability and Sampling.
  • Malbor Asllani University of Namur, Namur Institut for Complex Systems naXys (Abstract)
    New insights for the pattern formation paradigm in real life networks

    Order from disorder is a leitmotif in Nature that determined centuries of research efforts to unravel the underlying rules. Spatio-temporal patterns abound in real scenarios and the Turing mechanism has been for a long time a paradigm of self-organisation; the emergence of the resulting order has been elegantly grounded in a competition process between slow diffusing activators and fast diffusing inhibitors. Such separation of time scales is however often difficult to be achieved in real systems and this challenges one of the key principles of underlying Turing mechanism. Starting from the observation that interactions among constituting elements of real systems appear to be universally highly asymmetric, possessing a strong non-normality, we propose a new mechanism of pattern formation grounded on empirical evidence. We propose that one should not focus on the local interactions but on the non-local network of interactions within a system.


Registration is now open. Please submitt your abstract (plain text) through: EasyChair

Registration to CCS2018 is required in order to attend the workshop. There are no additional costs.

Important dates

Abstract submission deadline: 30 June 2018

Notification of acceptance: 10 July 2018

Workshop: 26 September 2018


26 September 2018

Cooming soon


The workshop will take place at

Vellidio Convention Center
Leoforos Stratou 3, Thessaloniki 546 39, Greece

Previous Events

  • [SOP2016]:Self-organized patterns on complex networks. A satellite workshop of the CCS2016, Amsterdam, 21 September 2016.
  • [SOP2017]: Control of self-organized patterns on complex networks. A satellite workshop of the PhysCon2017, Florence, 18 July 2017.