Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data
Fig 6
Results of the continuous-time estimator run on a noisy copy process , where conditioning on a strong common driver M should lead to zero information flow being inferred.
The translation ω of the source, relative to the target and common driver, controls the strength of the correlation between the source and target (maximal at zero translation). For each translation, the estimator is run on both the original process as well as embeddings generated via two surrogate generation methods: our proposed local permutation method and a traditional source time-shift method. The solid lines show the average TE rate across multiple runs and the shaded areas span from one standard deviation below the mean to one standard deviation above it. The bias of the estimator changes with the translation ω, and we expect the estimates to be consistent with appropriately generated surrogates reflecting the same strong common driver effect. This is the case for our local permutation surrogates, as shown in (A). This leads to the correct bias-corrected TE value of 0, as shown in (B).