Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data
Fig 9
Results of both estimator and surrogate generation combinations being applied to data from simulations of a biophysical model of a neural circuit inspired by the pyloric circuit of the crustacean stomatogastric ganglion.
The circuit, shown in (A), is fully connected apart from the missing connection between the PY neuron and the AB/PD complex, and generates membrane potential traces which are bursty and highly-periodic with cross-correlated activity. The distribution of p values from the combination of the continuous-time estimator and local permutation surrogate generation scheme are shown in (C). They demonstrate that this combination is capable of correctly identifying the conditional dependence and independence relationships in this circuit in all runs, apart from two false negatives. By contrast, the distribution of p values produced by the combination of the discrete-time estimator and the traditional source time-shift surrogate generation method shown in (D) mis-specified the relationship from the PY to the ABPD in every run. Ticks represent the particular combination of estimator and surrogate generation scheme making the correct inference of dependence or independence in the majority of cases when a cutoff value of p = 0.05 is used.