Using a cellular neural network based olfactory bulb model for choosing the best sensor temperature for an odor classification problem

In this paper, a part of mamal olfaction system, olfactory bulb, is modelled by a Cellular Ceural Network and the performance of the model in an odor classification problem is evaluated for different sensör temperatures in order to figure out in which sensör temperature the most distinguishable data is recorded. The relevant probem in odor classification task is the slowy changing time response of the odor sensors and the model presented in this work is a structure that can be used to speed up odor processing.