Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference
Fig 11
Dimension reduction of the PN response to a stimulus.
(A) Data as presented in [7] showing the trajectories in the first three components of principal component analysis for three different odors (different plots) and three stimulus durations for each odor (blue, red and green for 0.3s, 1s and 3s, respectively). The dynamic phase of activity lies between baseline activity (represented by B) and the fixed point (FP), where a number of turning points can be seen, between 2 and 4 for different cases. (B) First three elements of principal components analysis of the data obtained with the proposed model of the 30 PNs during the first phase of the full response, for different number of clusters in a sequence (4, 6 and 10 clusters). The number of elements equals the number of sharp turning points in the trajectory (big circles in the left-most plot). Around each turning point, the data points agglomerate, reflecting the period in which a cluster remains active. Since we used data from only the onset sequential phase, the trajectories we present are not closed as in experimental data where the data acquisition is done up until the system returns to baseline, a few seconds after odor offset. The points B and FB, as before, represent the baseline (at odor onset) and the fixed point (end of onset sequential phase).