Our goal is to understand the neural basis of perception, using theoretical and computational models of sensory systems. These models connect the physiological level (properties of neurons) with the behavioral level. Thus theories are tested with physiological experiments (in particular electrophysiology) and behavioral experiments (psychophysics). They are also tested from a computationalperspective, by evaluating the functional performance of models in complex perceptual tasks.

Our research is organized around three broad themes:

1) Neurons

We develop predictive neuron models, i.e., models that can predict the response of a neuron (action potentials) to a sensory stimulus (in vivo) or to an injected current (in vitro). We are interested in particular in neural excitability (adaptation and plasticity) and in the spatial aspect of spike initiation (initiation in the axon).

2) Perceptual systems

We are investigating the neural basis of perception, in particular the perception of space (visual and auditory), in complex ecological environments. We try to characterize the structure of ecological environments, and we develop neural models in which selective synchronization of spikes produced by neurons reflects the detection of a structure in the sensory flow. These models are then testedby their ability to perform complex tasks in ecological environments, and by experiments (in vivo electrophysiology and psychophysics).

3) Simulation technology

We develop an open source neural network simulator, Brian (http://briansimulator.org), which was designed to allow quick development of new models, with little constraint on the type of models. We are extending this technology to fast simulation on parallel platforms.