The problem of emergent synchronization patterns in a complex network of coupled oscillators has caught scientists' interest in a lot of different disciplines. In particular, from a biological point of view, considerable attention has been recently devoted to the study of the human brain as a network of different cortical regions that show coherent activity during resting-state. In literature, there can be found different large-scale models of resting-state dynamics in health and disease. In this context, the Kuramoto model, a classical model apt to describe oscillators' dynamics, has been extended to capture the spatial displacement and the communication conditions in such brain network. Starting from a previous work in this field [1], we analyze this modified model and compare it with other existing large-scale models. In doing so, our aim is to promote a set of mathematical tools useful to better understand real experimental data in neuroscience and estimate brain dynamics.

On brain modeling in resting-state as a network of coupled oscillators

FAVARETTO, CHIARA;CENEDESE, ANGELO
2016

Abstract

The problem of emergent synchronization patterns in a complex network of coupled oscillators has caught scientists' interest in a lot of different disciplines. In particular, from a biological point of view, considerable attention has been recently devoted to the study of the human brain as a network of different cortical regions that show coherent activity during resting-state. In literature, there can be found different large-scale models of resting-state dynamics in health and disease. In this context, the Kuramoto model, a classical model apt to describe oscillators' dynamics, has been extended to capture the spatial displacement and the communication conditions in such brain network. Starting from a previous work in this field [1], we analyze this modified model and compare it with other existing large-scale models. In doing so, our aim is to promote a set of mathematical tools useful to better understand real experimental data in neuroscience and estimate brain dynamics.
2016
2016 IEEE 55th Conference on Decision and Control, CDC 2016
55th IEEE Conference on Decision and Control, CDC 2016
9781509018376
9781509018376
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3221307
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