Computational neuroscience of sensory systems


Research center

17 rue Moreau
75012 Paris
José-Alain Sahel


Université Pierre et Marie Curie


UMRS968 UMR7210 UM80
Labex LIFESENSES, Label "Institut Carnot", Label CTRS / RTRS "Fondation Voir & Entendre", Qualification de « Projet Structurant » par Medicen Paris Région, Labellisation par la Foundation Fighting blindness


computational neuroscience
sensory systems
spike initiation
neural simulation
spike timing


Benichoux V, Rébillat M, Brette R.On the variation of interaural time differences with frequency.J Acoust Soc Am. 2016 Apr;139(4):1810. doi: 10.1121/1.4944638.

#Laudanski J, #Zheng Y, #Brette R (2014). A structural theory of pitch. eNeuro (in press).

#Rébillat M, #Benichoux V, Otani M, Keriven R, #Brette R (2014). Estimation of the low-frequency components of the head-related transfer functions of animals from photographs. JASA, 135, 2534 (2014).

#Fontaine B, Peña JL, #Brette R (2014). Spike-threshold adaptation predicted by membrane potential dynamics in vivo. PLoS Comp Biol, 10(4): e1003560.

#Stimberg M, #Goodman DFM, #Benichoux V, #Brette R (2014).Equation-oriented specification of neural models for simulations. Frontiers Neuroinf, doi: 10.3389/fninf.2014.00006.

#Goodman DFM, #Benichoux V, #Brette R (2013). Decoding neural responses to temporal cues for sound localization. eLife2:e01312.

#Brette R (2013). Sharpness of spike initiation in neurons explained by compartmentalization. PLoS Comp Biol, doi:10.1371/journal.pcbi.1003338.

Fields of research

Computational neurosciences / neural theory

Research Theme

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 (, 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.