Prediction of spikes | Log likelihood ratios (relative to a homogeneous Poisson model ) increase monotonically with the increasing fraction of observed inputs (Fig. |
Quantifying accuracy and detecting functional connections | For instance, when H2 is a homogeneous Poisson model that only describes the mean firing rate, the log likelihood ratio quantifies how much more accurately spikes are predicted by the model H1 over just predicting the mean. |
U | Both Model 1 with spike history-only and Model 2 with coupling tend to over-fit data from recordings shorter than ~ 5—10s (LLR<0 when compared to a homogeneous Poisson model which describes only the baseline firing; Fig. |