We want to understand how sensory signals are processed by neural devices such as the human or the insect brain in order to drive the animal's behaviour. By 'understand' we mean being able to account for all measurable aspects of sensory behaviour through simple models consisting of a small number of elements embedded within a physiologically plausible circuit.
We are not interested in processes that cannot be measured; for this reason, the first step in our enquiry consists of a thorough and extensive empirical characterization of the specific process under investigation. Data are then used to constrain fully specified computational models which we assess using simple mathematics or computer simulations.
The two steps of 1) characterizing the process experimentally on the one hand, and 2) accounting for the empirical results via computational models on the other hand, are not in our view separable. We strive to integrate the two approaches as closely as possible for each project and each researcher. We believe it is critical that the same individual understands and handles both steps. Outsourcing either one to others belittles the complexity of both, as they mutually inform each other in ways that are best exploited only by understanding this complexity at both levels at the same time.
We approach the sensory process by first describing it as a treatable mapping between the input stimulus and the behavioural decision. We then focus our efforts on two specific sensory systems, vision and audition. We attempt to estimate the perceptual filter underlying the sensory process used by the observer to detect a specified signal; for this purpose we rely on recent technical developments in psychophysics, although some of our projects use more classic threshold-based approaches.
The resulting characterization is often as detailed as is feasible given realistic constraints on the number of participants and the amount of data collected by a given participant. This characterization is used to guide the implementation of computational models, typically consisting of a front-end physiologically plausible circuit feeding into a standard signal detection theory decision model. We have used this approach to study a number of phenomena in human vision and audition, ranging from low-level feature detection to natural images and sounds; please refer to our list of publications for details.