Predicting computations necessary for the decoding of odor mixtures by the olfactory system

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Our olfactory system processes complex odor mixtures, drawn from a very high dimensional space of over 10,000 possible odorants, using a limited set of (~100-1000) olfactory receptors. While a considerable amount of work has done to understand the odors are encoded by the olfactory receptors, the inverse problem: “How does the olfactory system obtain odor information from receptor response?” is unclear. This project aims to identify the computations that are necessary for the decoding odor information from receptor responses. We will develop decoding algorithms and mechanistic neural network models for decoding odor information from receptor responses. We will study the performance of these algorithms and mechanistic models and compare their predictions to the structure of the olfactory system and available data on the performance of organisms in olfactory behavioral tasks. Through such comparisons, we will identify the specific computations that are necessary for decoding of natural odors. This would be achieved through the following aims. Aim 1: Develop algorithms for decoding odor information from receptor responses and compare their performance to behavioral data. Aim 2: Develop biophysical neural network models the olfactory system and compare it to odor decoding algorithm to predict role of olfactory circuits.