A Neural Network Model of the Olfactory System for Glomerular Activity Prediction

Recently, the importance of odors has begun to be emphasized as well as methods for their evaluation, especially in the fragrance and food industries. Although odors can be characterized by their odorant components, their chemical information cannot be directly related to the flavors we perceive. Recent research has revealed that neuronal activity related to glomeruli (which form part of the olfactory system) is closely connected to odor qualities. In this paper, we propose a neural network model of the olfactory system in mice to predict glomerular activity from odorant molecules. To adjust the parameters included in the model, a learning algorithm is also proposed. The results of simulation proved that the relationship between glomerular activity and odorant molecules could be approximated using the proposed model. In addition, the model could predict glomerular activity to a certain extent. These results suggest that the proposed model could be utilized to predict odor qualities for future application.