The Thirteenth Israeli Mini-Workshop in Applied
and Computational Mathematics
Eli Shlizerman (Applied Math, University of Washington)
Competing spatiotemporal neural codes in the olfaction of the Manduca sexta moth
Experiments across different species have shown that perception of odors in the olfactory system is associated with neural encoding patterns. The neurobiological mechanisms responsible for such encoding patterns, their transient dynamics and interactions are yet to be fully understood. We show that a data-driven computational model reduction for the antennal (olfactory) lobe (AL) of the Manduca sexta moth reveals the nature of experimentally observed persistent spatial and temporal neural encoding patterns and its associated decision-making dynamics. Utilizing the experimental data we reduce a high-dimensional neural network model of the AL to a decision making model. Analyzing the model we conclude that the mechanism responsible for the robust and persistent appearance of neural codes is a stable fixed point. The model is used to explain, predict and direct experiments when odors are mixed or the structure of the network is altered.
This is a joint work with Jeffrey Riffell and J. Nathan Kutz.
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