Exponential inequalities and local independence graphs to unravel functional neuronal connectivity

Add to your list(s) Download to your calendar using vCal

• Patricia Reynaud-Bouret, Université de Nice Sophia-Antipolis
• Tuesday 24 June 2014, 11:30-12:00
• Centre for Mathematical Sciences, Meeting Room 2.

If you have a question about this talk, please contact clc32.

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.

This talk is included in these lists:

• All CMS events
• All Talks (aka the CURE list)
• CMS Events
• Centre for Mathematical Sciences, Meeting Room 2
• DPMMS Lists
• DPMMS info aggregator
• DPMMS lists
• Hanchen DaDaDash
• Interested Talks
• School of Physical Sciences
• Statistical Laboratory info aggregator
• bld31

Note that ex-directory lists are not shown.