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. 2010 May 21;5(5):e10644. doi: 10.1371/journal.pone.0010644

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Satoh et al.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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Two different inhibition mechanisms that distinguish odors as represented in panels (A)(B) and in panels (C)(D). Circles and crosses are simulated data for PN responses to two different odor pairs obtained from our network model. Responses of 24 PN types to the odors are projected onto a space defined by the first two principal components. Solid lines show the decision boundaries of SVM classifiers learned from training data. Test data are plotted. Large crosses at the bottom of the figures represent the points where all PNs were silent. Large circles at the top represent the points where all PNs were firing. was set to the value at peak i in Fig. 10 (A) (). In panels (A) and (C), there was is no LN input (). In panels (B) and (D), was set to the value at peak i in Fig. 10 (A) ().

Inline graphic — Two different inhibition mechanisms that distinguish odors as represented in panels (A)(B) and in panels (C)(D). Circles and crosses are simulated data for PN responses to two different odor pairs obtained from our network model. Responses of 24 PN types to the odors are projected onto a space defined by the first two principal components. Solid lines show the decision boundaries of SVM classifiers learned from training data. Test data are plotted. Large crosses at the bottom of the figures represent the points where all PNs were silent. Large circles at the top represent the points where all PNs were firing. was set to the value at peak i in Fig. 10 (A) (). In panels (A) and (C), there was is no LN input (). In panels (B) and (D), was set to the value at peak i in Fig. 10 (A) ().