A Dual Algorithm for Olfactory Computation in the Locust Brain

We study the early locust olfactory system in an attempt to explain its well-characterized structure and dynamics. We first propose its computational function as recovery of high-dimensional sparse olfactory signals from a small number of measurements. Detailed experimental knowledge about this system rules out standard algorithmic solutions to this problem. Instead, we show that solving a dual formulation of the corresponding optimisation problem yields structure and dynamics in good agreement with biological data. Further biological constraints lead us to a reduced form of this dual formulation in which the system uses independent component analysis to continuously adapt to its olfactory environment to allow accurate sparse recovery. Our work demonstrates the challenges and rewards of attempting detailed understanding of experimentally well-characterized systems.