Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference
Fig 8
Performance of the model under adverse conditions.
(A) Simulations to test robustness against noise in the PNs. Percentage of correct trials (i.e. trials in which the model displayed the correct KC representation) for low SNRs. For these simulations, we added white noise of different variances to all PN activity. The model is said to display the correct response if the Euclidean distance in the firing-rate-space between the inferred and expected (i.e. used to create the data) KC responses is smaller than a threshold (see main text). A decrease in performance can be observed only for SNRs smaller than 2.6. (B) Simulations to test robustness against KC states not at baseline at the onset of an odor. Histogram of reaction times (see Methods) for trials with initial KC states set to a baseline (light gray) or a random state (blue). These reaction times were calculated for two completely dissimilar sequences with high SNR. Therefore recognition is rapid. The reaction times for trials with random initial conditions are slightly more varied and the mean reaction time is 19ms, as compared to 17ms when the initial KC activity is at baseline.