Mesoscale Two-Photon Calcium Imaging of Population Level Odor Responses from the Mouse Olfactory Bulb

This study explores odor-evoked activity representation in the mouse olfactory bulb (OB) using mesoscale two-photon calcium imaging to investigate how odor responses enable discrimination. Contrary to theories suggesting purely sparse representations, we hypothesize a denser population-level representation during odor presentation. The dataset includes population-level odor responses recorded as NWB files containing TwoPhotonSeries (raw imaging data), SpatialSeries, and resulting PlaneSegmentation/ProcessingModules from calcium signal extraction. By applying machine learning techniques, we suggest a model in which sparse coding is sufficient for olfaction, but redundant, dense information makes odor coding robust across different variables. Data includes behavioral traces and surgical techniques associated with mesoscale imaging of the OB.