We want to understand how sensory signals are processed by neural devices (e.g. human, insect, or fish brain) to control and drive behaviour. By "understand" we mean: account for all measurable aspects of sensory behaviour (which we characterize to exhaustive detail given realistic constraints on data mass acquisition) via parsimonious computational models, often consisting of a few elements embedded within a physiologically plausible circuit. These models are assessed using mathematics or computer simulations. We have used this approach to study a wide range of sensory operations (please refer to our list of publications).
The two steps of 1) characterizing the process experimentally on the one hand, and 2) accounting for the empirical results via theory/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.
What does it mean to "understand" a visual operation? A gallery of algorithms and circuits, and a brief explanation of why they work.
How do nets compare with humans when extracting features from natural scenes, detecting signals in noise, and looking at abstract art?
The remarkable noisiness of human behaviour, its statistical distribution, and its surprising relationship with the calculus of variations and zebrafish.
From single spikes to circuit models.
Feature conjunction in zebrafish? Yes. Complex visual analysis in the fighter-fish? Again, yes!
How do we combine local motion signals to make out the complex movements generated by others? What does this have to do with mirror neurons, or autism?
Recording signals from the human brain, striking a balance between spatial resolution (fMRI) and temporal resolution (EEG).