ANR PredictEye (2018/2020)

The objectives of PREDICTEYE is to rigorously test and define the functional and neurophysiological grounds of probabilistic oculomotor internal models by investigating the multiple timescales at which the trajectory of a moving target is learned and represented in a probabilistic framework (Aim #1). Second, we will investigate the role of (pre)frontal oculomotor networks in building such probabilistic representations and their impact upon two of their downstream neural targets of the brainstem premotor centers (superior colliculus for saccades; NRTP for pursuit) (Aim #2). Our third objective is to model and simulate the dynamics of target motion prediction and eye movement performance. A key question is to unveil how probabilistic information about target timing and motion (i.e. direction and speed) is sampled over trial history by neuronal populations and integrated with Prior knowledge (i.e. sequence properties and rules of conditional probabilities) in order to coordinate saccades and pursuit and optimize their precisions (Aim #3).

• ANR-2018 Project PREDICTEYE - Agence Nationale de la Recherche (2018-2022). This project starts november 2018, for 4 years. It will investigate the neural networks in human volunteers supporting anticipatory pursuit eye movements using magnetic transcranial stimulation (TMS) to perturb frontal networks during ocular tracking of predictable targets. In complementary studies conducted in macaque monkeys, perturbations will be applied pharmacologically to subcortical targets of this frontal network, namely superior colliculus and NTRP, a pontine nucleus relaying information to the pursuit networks of the cerebellum. The project involves 4 CNRS permanent researchers from the INVIBE team headed by G Masson. The funding is 507K€ for 4 years. PI: G Masson, co-PI: A Montagnini, L Perrinet, L Goffart

• related grant by the Fondation pour le Recherche Médicale, under the program Équipe FRM (DEQ20180339203/PredictEye/PI: G Masson/ A. Montagnini and L. Perrinet as participants).

Acknowledgement

This work was supported by ANR project "PredictEye" ANR-XXXX.