Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction Conference Paper

abstract

• We present a model of olfactory bulb-cortex interaction for the purpose of mixture processing with gas sensor arrays. The olfactory bulb is modeled with a neurodynamic model whose lateral inhibitory connections are learned through a modified Hebbian-anti-hebbian rule. Bulbar outputs are then projected in a non-topographic fashion onto the olfactory cortex. Associational connections within cortex using Hebbian learning form a content addressable memory. Finally, inhibitory feedback from cortex is used to modulate bulbar activity. Depending on the form of feedback, Hebbian or anti-Hebbian, the model is able to perform background suppression or mixture segmentation. The model is validated on experimental data from a gas sensor array. 2005 IEEE.

name of conference

• Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

authors

• Gutierrez-Osuna, Ricardo

published proceedings

• Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5

author list (cited authors)

• Raman, B., & Gutierrez-Osuna, R.

citation count

• 11

complete list of authors

• Raman, B||Gutierrez-Osuna, R

publication date

• January 2005

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE International Conference on Neural Networks - Conference Proceedings  Journal

Digital Object Identifier (DOI)

• 10.1109/ijcnn.2005.1555818

International Standard Book Number (ISBN) 10

• 0-7803-9048-2

International Standard Book Number (ISBN) 13

• 9780780390485

start page

• 131

end page

• 136

volume

• 1

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fijcnn.2005.1555818