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

@inproceedings{citeulike:2789576, 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 = {Raman, B. and Gutierrez-Osuna, R. }, booktitle = {Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on}, citeulike-article-id = {2789576}, doi = {http://dx.doi.org/10.1109/IJCNN.2005.1555818}, journal = {Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. IJCNN '05}, keywords = {network, neural, neurodynamics, odor, sensor}, pages = {131--136}, posted-at = {2008-05-12 15:16:23}, priority = {0}, title = {Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction}, url = {http://dx.doi.org/10.1109/IJCNN.2005.1555818}, volume = {1}, year = {2005} }

TY - CONF ID - citeulike:2789576 L3 - citeulike-article-id:2789576 TI - Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction T2 - Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on JF - Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. IJCNN '05 VL - 1 SP - 131 EP - 136 N2 - 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. KW - network KW - neural KW - neurodynamics KW - odor KW - sensor AU - Raman, B AU - Gutierrez-Osuna, R PY - 2005/// UR - http://dx.doi.org/10.1109/IJCNN.2005.1555818 ER -