Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference
Fig 9
Time-jittered KC activity from the model.
Using one connectivity matrix, we generated PN data with no jitter and used that data to create time-jittered PN activity. The KC activity output by our model is shown: each row represents a sequence displayed by our model; each color represents a cluster in the sequence. The first sequence (top) has no time-jitter in the input. The following sequences are time-jittered, i.e. the time each cluster is active is different. It can be readily seen that the Bayesian inference has no trouble handling time-jitter that cannot be generated directly by the generative model.