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


  • 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.

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