University of Pennsylvania, USA
Becoming what you smell: adaptive sensing in the olfactory system
I will discuss the circuit architecture of the early olfactory system as an adaptive, efficient mechanism for compressing the vast space of odor mixtures into the responses of a small number of sensors. In this view, the olfactory sensory repertoire first leverages the power of randomness to implement a compressive sensing procedure. It then adapts the resulting representation to efficiently encode and optimize information transfer from the changing environment of volatile molecules. I will show how such adaptive, combinatorial information representations can be efficiently decoded, and will compare the predictions with animal behavior. The resulting algorithm for “estimation by elimination” can be implemented by a neural network that is remarkably similar to the early olfactory pathway in the brain. The theory predicts a relation between the diversity of olfactory receptors and the sparsity of their responses that matches animals from flies to humans. It also predicts specific deficits in olfactory behavior that should result from optogenetic manipulation of the olfactory bulb.