Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference
Fig 10
Performance of the model with noisy PN input.
The performance of our model was measured in terms of displaying the expected sequence in response to the input while a number of PNs that connect to a single KC display large amounts of noise. Three noise levels were used (different colors); with high noise (SNR of only 1), the activity of a PN is mostly noise-driven, because the fluctuations due to noise are comparable to activity of the noise-free PNs. With an SNR of 2 (called here low noise), the noise variations are about one third of the maximum noise-free PN activity. Medium noise had an SNR of 1.5. On the x-axis, we plotted the number of noisy PNs; on the y-axis, the percentage of trials in which the KCs displayed the correct representation (100 trials for each number of noisy PN/noise combination). Left: performance of an inverted Single-neuron SHS generative model. We used a modified version of our generative model, in which single KCs, as opposed to clusters of KCs, are activated sequentially. This is then projected to the PNs as described for our proposed model and inverted with the described unscented Kalman filter. Right: performance of proposed model with Cluster SHS. The large differences in the performance of the two models can be attributed to the within-cluster lateral input that the KCs receive (i.e. intelligent coincidence detector) in the Cluster SHS model: The performance of the single-neuron SHS model degrades for smaller number of noisy PNs, for all noise levels. In particular, for high noise rather small numbers of noisy PNs lead to poor performance.