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

B. Raman; R. Gutierrez-Osuna

doi:10.1109/IJCNN.2005.1555818

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

B. Raman, R. Gutierrez-Osuna

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages	131-136
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2005.1555818
State	Published - 2005
Event	International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada Duration: Jul 31 2005 → Aug 4 2005

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	1

Conference

Conference	International Joint Conference on Neural Networks, IJCNN 2005
Country/Territory	Canada
City	Montreal, QC
Period	07/31/05 → 08/4/05

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10.1109/IJCNN.2005.1555818

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Raman, B., & Gutierrez-Osuna, R. (2005). Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction. In Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005 (pp. 131-136). [1555818] (Proceedings of the International Joint Conference on Neural Networks; Vol. 1). https://doi.org/10.1109/IJCNN.2005.1555818

@inproceedings{eab44e9e08d64362904d390c6cf14051,

title = "Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction",

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.",

author = "B. Raman and R. Gutierrez-Osuna",

year = "2005",

doi = "10.1109/IJCNN.2005.1555818",

language = "English",

isbn = "0780390482",

series = "Proceedings of the International Joint Conference on Neural Networks",

pages = "131--136",

booktitle = "Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005",

note = "null ; Conference date: 31-07-2005 Through 04-08-2005",

}

Raman, B & Gutierrez-Osuna, R 2005, Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction. in Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005., 1555818, Proceedings of the International Joint Conference on Neural Networks, vol. 1, pp. 131-136, International Joint Conference on Neural Networks, IJCNN 2005, Montreal, QC, Canada, 07/31/05. https://doi.org/10.1109/IJCNN.2005.1555818

Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction. / Raman, B.; Gutierrez-Osuna, R.

Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005. 2005. p. 131-136 1555818 (Proceedings of the International Joint Conference on Neural Networks; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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BT - Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005

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

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