Abstract
The demands for odor processing apparatuses have been increasing in fragrance or food industries. However, odors are extremely high dimensional information composed a combination from tens thousands of different odorant molecules, and thus requires vast amounts of computation. Therefore, it is considered learning from a living nose would be an efficient approach. From the odor discrimination experiments, it was found that mice have a feature extraction ability called Attention by which they could focus on the important odorants for odor discrimination. In this paper we propose a neural network model approximated to actual number of neurons and the structure of olfactory system. Simulation experiments of the proposed model were implemented based on the odor discrimination experiments on the living mice. From the simulation results of the model, we confirmed not only the proposed model had ability of Attention, but also the tendency of Attention was consistent with the living mice.
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This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008
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Soh, Z., Tsuji, T., Takiguchi, N. et al. A neural network model of the olfactory system of mice: simulated the tendency of attention behavior. Artif Life Robotics 13, 280–285 (2008). https://doi.org/10.1007/s10015-008-0538-0
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DOI: https://doi.org/10.1007/s10015-008-0538-0