dynamics

Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network

see Kaplan and al, 2014

Axonal delays and on-time control of eye movements

Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed as fast as possible. One unsolved question is to understand how the brain pools motion information to give an …

Grabbing, tracking and sniffing as models for motion detection and eye movements

Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed as fast as possible. One unsolved question is to understand how the brain pools motion information to give an …

Motion-based prediction is sufficient to solve the aperture problem

In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent global percept. This is well demonstrated for the detection of motion in the aperture problem: as luminance of an …

Propriétés émergentes d'un modèle de prédiction probabiliste utilisant un champ neural

Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual system could describe superposed and ambiguous information. This allows to describe the interactions of neighboring …

Probabilistic models of the low-level visual system: the role of prediction in detecting motion

Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual system could describe superposed and ambiguous information. This allows to describe the interactions of neighboring …

Models of low-level vision: linking probabilistic models and neural masses

see this more recent talk @ UCL, London