Authors:
Zu Soh
1
;
Toshio Tsuji
2
;
Noboru Takiguchi
3
and
Hisao Ohtake
1
Affiliations:
1
Osaka University, Japan
;
2
Hiroshima University, Japan
;
3
Kanazawa University, Japan
Keyword(s):
Olfactory system, Attention, Neural network model.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Intelligent Artificial Perception and Neural Sensors
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Our research group has found behavioral evidence that an attention function exists in the olfactory system similarly to in the visual and auditory systems. In this paper we propose a neural network model that accounts for olfactory attention based on macroscopic neural connections. Specifically, on-center/off-surround connections were assumed to be involved in the attention process in accordance with our hypothesis of an attention window that extracts local activity. The model employs glomerular activity patterns as its input, and compares them with stored patterns focusing on their local activity. The model also can shift and change the attention window with respect to learning. From the simulation results, we confirmed that the model can account for the results of a behavioral experiment on olfactory attention in mice.