Probabilités et statistiques
Mean-field descriptions of coupled stochastic oscillators and its applications to Neuroscience
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Europe/Paris
Description
Mean-field models are one of the most important approaches to understanding the dynamics of complex systems. In neuroscience, they are useful both to understand networks of individual neurons, and as building blocks for whole-brain models. In both scenarios, models of coupled oscillators have demonstrated to be a flexible approach, which has gained popularity in recent years. However, most of our current mathematical machinery is devised for deterministic systems, not capturing the fluctuations at the macroscopic level.
In this talk, I will review modern approaches for systems of coupled oscillators and their implications for theoretical neuroscience, from individual neurons to whole-brain neural mass models and potential applications.
In particular, I will show a novel application for quadratic integrate-and-fire (QIF) neurons. The QIF model has become an extremely powerful tool in computational neuroscience since its publication in 2015, leading to the so-called "next-generation" neural field models.
In particular, I will show a novel application for quadratic integrate-and-fire (QIF) neurons. The QIF model has become an extremely powerful tool in computational neuroscience since its publication in 2015, leading to the so-called "next-generation" neural field models.