Max Planck Institute for Dynamics and Self-Organization -- Department for Nonlinear Dynamics and Network Dynamics Group
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Thursday, 07.05.2015 14:15 c.t.

Identification of criticality in brain

by Dr. Luc Berthouze
from University of Sussex, School of Engineering and Informatics, UK

Contact person: Viola Priesemann


Ludwig Prandtl lecture hall


The notion that the brain may operate at, or close to, a critical state is receiving much attention in the neuroscience community. Much of the work to date has relied on the characterisation of power law in some observable of the system, e.g., avalanche size, fluctuation of amplitude, inter-burst interval. The difficulty of such characterisation notwithstanding (I will present some of our work in this area), an outstanding research question is the extent to which these statistical observations actually justify inferring that the system is critical. I will start by describing our study of a simple model of a purely excitatory non-driven neural network that, by construction, operates at a critical point. This model allows us to consider various markers of criticality and illustrate how they should perform in a finite-size system. By calculating the exact distribution of avalanche sizes we are able to show that, over a limited range of avalanche sizes which we precisely identify, the distribution has scale free properties but is not a power law. We consider other possible markers, such as the divergence of susceptibility as the critical point of the system is approached. Next, I will present results when the same network is driven by a continuous external input, i.e., when the model does not have a separation of timescales. Derivation of the distributions of waiting times between neuronal avalanches shows that as the system approaches the critical state by two alternative `routes', different markers of criticality (partial scale-free behaviour and long-range temporal correlations) are displayed. This suggests that signatures of criticality exhibited by a particular system in close proximity to a critical state are dependent on the region in parameter space at which the system (currently) resides. I will conclude with recent work related to the identification of long-range temporal correlations in the moment-to-moment fluctuations of phase synchronisation in human brain. We found that brain resting states show synchronisation patterns with similar temporal structure to that of a system of Kuramoto oscillators just prior to its critical level of coupling.

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