Authors:
Zu Soh
1
;
Ryuji Inazawa
1
;
Toshio Tsuji
1
;
Noboru Takiguchi
2
and
Hisao Ohtake
3
Affiliations:
1
Hiroshima University, Japan
;
2
Kanazawa University, Japan
;
3
Osaka University, Japan
Keyword(s):
Glomerular activity prediction, Odor qualities, Olfactory system, Neural network model.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neuroinformatics and Bioinformatics
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
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 f
or future application.
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