Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference
Fig 4
Diagram of the proposed model.
The generative model was used in two ways: first, to generate the PN data that was used as input to the Bayesian inference; Second, to describe the internally expected dynamics of the PN and KC populations. Top left: Specifically, the generative model uses the Lotka-Volterra equations to generate activity of the KC population. Bottom left: This activity was projected into the PN population via Eq 2 giving synthetic PN activity that was used as input to the Bayesian inference. Bottom right: Through the unscented Kalman filter, the PN activity observed was balanced with the expected dynamics of the KCs (prescribed by the generative model). Top right: This enabled the model to infer KC activity that is consistent with the PN input data.