Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference
Fig 6
Performance of the model in difficult tasks.
The connectivity matrix has two sequences which were similar to each other. (A-B) Each single line represents one trial (out of 100 trials) simulated from the model. The Y axis represents the recognition variable, which is calculated from the difference between the Euclidean distances from the observed KC activity to the correct one and from the observed KC activity to the incorrect one (see Eq 6 in Methods). If the KCs are displaying the correct representation (encoding the displayed odor), the corresponding line is near the top. If the KCs are displaying the incorrect representation (i.e. the other sequence stored in the system), the corresponding line is near the bottom. Lines near the middle are as close to the correct representation as to the incorrect one. The activity of the clusters in the expected (correct) sequence is in the plot background as a time reference to show that many single trials quickly jump to the correct representation after the second cluster in the sequence starts: The shaded areas (2 colors) represent two clusters in the sequence (as in Fig 5B). (A) Results for a task in which the PN data for the two stored sequences are very similar during the first cluster: Only three out of twenty PNs are different for the two sequences during the first cluster. Due to noise, the KC activity represents sometimes the incorrect odor for a brief period of time. (B) Results for a task in which the PN data for the two stored sequences are identical during the first cluster but dissimilar for the rest of the odor. During exposure to the first segment of the odor, the representations on the KCs are correct around 50% of the time, as expected, consistent with random chance. When the second cluster in the sequence begins (at ca. 170ms), the KC activity quickly jumps to the correct representation. (C) Average reaction time of 100 trials plotted against difficulty of the task (as defined with the number of PNs that belong to the representations of both odors; see Methods). The PN representations of the two stored sequences are similar during the first 170ms; afterwards, they diverge and become easily identifiable. The more similar the PN representations of two stored odors are during this initial period, the longer it takes the KCs to identify it. The maximum reaction time, around 200ms, corresponding to the case in B, is obtained when all 20 PNs are the same. The case in A corresponds to 17 identical PNs.