In this study, we explore odor-evoked activity representation in the olfactory bulb (OB) and how odor responses enable odor discrimination. Contrary to some of the previously cited theories that suggest a sparse representation and largely rely on recordings from anesthetized animals, we hypothesize a more dense representation during and after odor presentation. A key question is how odors are reliably encoded in complex OB activity patterns, and how these patterns contribute to early odor processing. To address this problem, we recorded population level odor responses from the mouse OB with meso-scale two photon calcium imaging and applied machine learning and statistical analysis to suggest a model in which sparse coding is largely sufficient for olfaction, but redundant information may make odor coding more robust across different internal and environmental variables.