Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference
Fig 3
Illustration of experimental findings.
(A) Raster plots for the full response (three phases) to a stimulus of PNs (left) and KCs (right) using a modified version of the generative model (see Methods for details; also see the S1 Text). This modified version was only used for the generation of this figure and no inversion was performed on it. Although the model consisted of 30 PNs and 100 KCs, most KCs were inactive during the simulations. Only the 27 active KCs are shown. The simulation was run for ten trials and the responses of different neurons are separated by thick black lines. For the PNs, periods of inhibition and excitation can be observed. During the steady state phase the population no longer evolved. KC responses are very sparse, with only a few spikes per KC throughout a trial. (B) Instantaneous firing rate for a sample PN. (C) The periods of excitation and inhibition (with respect to a baseline) during the dynamic phase for the PN in B. The baseline shown is calculated as half the minimum PN activity that the generative model can produce before it is zero.