University of Cambridge > Talks.cam > Probability Theory and Statistics in High and Infinite Dimensions > Exponential inequalities and local independence graphs to unravel functional neuronal connectivity

Exponential inequalities and local independence graphs to unravel functional neuronal connectivity

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The starting theoretical point is to provide “data-driven” exponential inequalities for counting processes with as few assumptions as possible on the underlying conditional intensity. This allows us to present a weighted Lasso method in the abstract set-up of linear counting processes. Thanks to this method, it is possible to approximate real neuronal data, called spike trains, by Hawkes models even if this model is not true. This leads to an estimation of local independence graphs for which we are currently testing the adequation with the biological notion of “functional connectivity”.

This talk is part of the Probability Theory and Statistics in High and Infinite Dimensions series.

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