Center for Computational Biology, Flatiron Institute, New York, United States; Initiative for the Theoretical Sciences, The Graduate Center, City University of New York, New York, United States; David Rittenhouse Laboratories, University of Pennsylvania, Philadelphia, United States
Simona Cocco
Laboratoire de Physique Statistique, École Normale Supérieure and CNRS UMR 8550, PSL Research, UPMC Sorbonne Université, Paris, France
Initiative for the Theoretical Sciences, The Graduate Center, City University of New York, New York, United States; David Rittenhouse Laboratories, University of Pennsylvania, Philadelphia, United States
Olfactory receptor usage is highly heterogeneous, with some receptor types being orders of magnitude more abundant than others. We propose an explanation for this striking fact: the receptor distribution is tuned to maximally represent information about the olfactory environment in a regime of efficient coding that is sensitive to the global context of correlated sensor responses. This model predicts that in mammals, where olfactory sensory neurons are replaced regularly, receptor abundances should continuously adapt to odor statistics. Experimentally, increased exposure to odorants leads variously, but reproducibly, to increased, decreased, or unchanged abundances of different activated receptors. We demonstrate that this diversity of effects is required for efficient coding when sensors are broadly correlated, and provide an algorithm for predicting which olfactory receptors should increase or decrease in abundance following specific environmental changes. Finally, we give simple dynamical rules for neural birth and death processes that might underlie this adaptation.
