Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference
Fig 2
PN output of our model compared to the Laurent-Rabinovich model.
Firing rates of 100 KCs and 30 PNs in mushroom body and the antennal lobe, respectively, for the proposed model and an implementation of the Laurent-Rabinovich model. Each panel represents the activity of the KC and PN populations at times t1, t2, t3 and t4, in which the clusters of the sequence are at their maximum value (see Fig 5), and each square in a panel represents a neuron (10x10 for KCs, 6x5 for PNs). (A) An SHS model, as presented in [11], used for the KC population, plus a random 1 KC to 20 PNs projection. (B) Data generated by the proposed model, with clusters of three KCs each.