Workshop Statistical methods for neuronal data
Organization Patricia Reynaud-Bouret (Laboratoire Dieudonné, Université Nice) et Adeline Samson (Laboratoire MAP5, Université Paris Descartes) Lieu Salle du Conseil, Aile Turing, 7th floor Université Paris Descartes 45 rue des Saint Pères 75006 Paris Programme 9:00-9:15 Welcome 9:15-9:50 Christine Tuleau-Malot (Université de Nice) {Estimation and goodness of fit tests for point [...]
Organization
Patricia Reynaud-Bouret (Laboratoire Dieudonné, Université Nice) et Adeline Samson (Laboratoire MAP5,
Université Paris Descartes)
Lieu
Salle du Conseil, Aile Turing, 7th floor
Université Paris Descartes
45 rue des Saint Pères
75006 Paris
Programme
9:00-9:15 Welcome
9:15-9:50 Christine Tuleau-Malot (Université de Nice) {Estimation and goodness of fit tests for point processes used to model neuronal activity}
9:50-10:25 Maureen Clerc (INRIA Sophia-Antipolis)/Olivier Renaud (Genève)
TBA
10:45-11:20 Patricia Reynaud Bouret (Université de Nice)/Vincent Rivoirard (Université Paris Dauphine) Estimation of local dependence graphs via Hawkes processes
11:20-11:55 Daniel Takahashi (Princeton) Coupled oscillator dynamics of vocal turn-taking in monkeys
11:55-12:30 Mathieu Lerasle (Université de Nice) Estimation of the potential function in Ising models, with applications to neural networks
14:00-14:35 Christophe Pouzat (Université Paris Descartes) How do neurophysiologists get the data you are now analyzing?
14:35-15:10 Rune Berg (University of Copenhagen) Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations
15:10-15:45 Massimiliano Tamborrino (University of Copenhagen) Parametric inference of Leaky-Integrate-and-Fire Models from interspike intervals where a stimulus occurs
16:00-16:35 Anders Jensen (University of Copenhagen) Parameter estimation in multidimensional diffusions with applications to the FitzHugh-Nagumo model
16:35-17:10 Susanne Ditlevsen (University of Copenhagen)/Adeline Samson (Université Paris Descartes) Estimation in the Partially Observed Stochastic Morris-Lecar Neuronal model with Particle Filter and Stochastic Approximation Methods