Quantification of the network strength in neural anticipated and delayed synchronization

MONSERRAT PALLARES DI NUNZIO; NATALI GUISANDE; MAURO GRANADO; FERNANDO MONTANI

Conferencia; CNS 2021 / Computational Neuroscience 2021; 2021

Organization for Computational Neuroscience https://www.cnsorg.org/

The phenomenon of synchronization between two or more asymmetrically coupled brain areas is a very relevant topic for understanding the mechanisms and functions within the cerebral cortex. Anticipatory synchronization (AS) refers to the situation in which the receiving system, referred to as the ´slave´, synchronizes with the future dynamics of the sending system, referred to as the ´master´. In contrast, delayed synchronization (DS) represents the intuitively opposite case. In this work we investigate and compare the magnitude of connection between simulated neural networks in AS and DS regimes making use of causal information and calculating the Jensen-Shannon divergence through a symbolic formalism of ordinal patterns. By studying multiple temporal scales, it could be observed that Jensen-Shannon divergence is bigger for the AS regime than the DS regime, which means that AS has a lower magnitude of connection than the DS regime. Furthermore, this formalism allows us to successfully discern the dynamical characteristics that differ in these two synchronization cases.