Rounding of abrupt phase transitions in brain networks
Villa Martín P, Moretti P, Muñoz MÁ (2015)
Publication Language: English
Publication Status: Published
Publication Type: Journal article, Original article
Publication year: 2015
Journal
Publisher: Institute of Physics: Hybrid Open Access
Book Volume: 2015
Article Number: P01003
Journal Issue: 1
DOI: 10.1088/1742-5468/2015/01/P01003
Abstract
The observation of critical-like behavior in cortical networks represents a major step forward in elucidating how the brain manages information. Understanding the origin and functionality of critical-like dynamics, as well as its robustness, is a major challenge in contemporary neuroscience. Here, we present an extensive numerical study of a family of simple dynamical models, which describe activity propagation in brain networks through the integration of different neighboring spiking potentials, mimicking basic neural interactions. The requirement of signal integration may lead to discontinuous phase transitions in networks that are well described by the mean-field approximation, thus preventing the emergence of critical points in such systems. Brain networks, however, are finite dimensional and exhibit a heterogeneous hierarchical structure that cannot be encoded in mean-field models. Here we propose that, as a consequence of the presence of such a heterogeneous substrate with its concomitant structural disorder, critical-like features may emerge even in the presence of integration. These conclusions may prove significant in explaining the observation of traits of critical behavior in large-scale measurements of brain activity.
Authors with CRIS profile
Involved external institutions
Spain (ES)How to cite
APA:
Villa Martín, P., Moretti, P., & Muñoz, M.Á. (2015). Rounding of abrupt phase transitions in brain networks. Journal of Statistical Mechanics-Theory and Experiment, 2015(1). https://doi.org/10.1088/1742-5468/2015/01/P01003
MLA:
Villa Martín, Paula, Paolo Moretti, and Miguel Ángel Muñoz. "Rounding of abrupt phase transitions in brain networks." Journal of Statistical Mechanics-Theory and Experiment 2015.1 (2015).
BibTeX: Download