My teaching expertise is in understanding and modeling vision, specifically with respect to color and shape perception, attention and sensory-motor integration. The techniques that I master are psychophysics and whole brain imaging. This includes classical signal detection approaches as well as more recently developed approaches related to Bayesian inference, such as, Predictive Coding models in visual perception I can additionally provide courses and tutorials in digital and image processing.

I am particularly interested in encouraging students from diverse scientific backgrounds (human sciences, physiology, engineering-signal processing…) to appreciate the value and impact of computational neuroscience in research: how to choose and develop the correct statistical models for behavioral and physiological data analyses, experimental design and the construction of experimental protocols and model-based quantitative testing for framing hypotheses on neural function and mechanism.

Quantitative approaches based on theory, mathematics and statistics sharpen the questions that can be posed about function and structure of the brain, such as how information is encoded in specific circuits, the principles of sensory coding, mid-level perception and higher order cognitive functions. I can also provide technical courses in programming in languages such as Matlab and R, for data analyses and modeling (psychophysics and fMRI) with respect to approaches such as signal detection theory, pattern classification (support vector machines) and dynamic causal modeling.


I have taught courses in France, at the Swiss Federal Institute of Technology in Lausanne (Switzerland) and the University of Birmingham (U.K.) in Neurosciences, Computer Science & Psychology Dept (155 hours in total).

Thema: Brain mapping, Psychophysics, Visual system, Color vision, Image processing, Statistics.



Current student under my supervision


PhD student

Since 2014 Clément Abbatecola Integrating voice and face in human: psychophysical studies and functional imaging.


Master students

Since 2018 Kim Beneyton Neural correlates face and voice integration with psychophysics and fMRI.

Since 2017 Julien Fars Bayesian Inference and Object Perception.

Bayesian Inference in Perception

2017 University of Lyon, France

Neurosciences Dept

Master 2 Neurosciences Fondamentales & Cliniques

UE Neural Basis of Cognition (Master level)

This lecture presents the hierarchical nature of information processing in the brain in terms of structure and function (in particular perception and cognition), in the context of several current models of perceptual organization. Initially, I describe hierarchical structural relations in the cortex: the anatomical signatures of feedback and feedforward pathways and evidence for functional signatures of these pathways in the human brain and animal models. The course focuses on behavioral evidence that the brain generates hypotheses about what it perceives. This process accounts for many perceptual illusions, and is well described within a Bayesian framework. Perceptual inference involves hierarchical processing at multiple levels, and we, therefore, explore how brain imaging can be exploited to reveal functional hierarchical relations between brain regions. We discuss the Theory of Predictive Coding and some of the evidence that supports it. This theory describes the dynamics of hierarchical processes, the role of surprise and error minimization in perceptual and cognitive processes. It also proposes explanations for anomalies of perception that occur in cases of psychiatric disorders that can be accounted for by dysfunctional hierarchical processing.