Depending on input statistical structure, granule cells exhibit pattern separation or convergence via burstiness and firing rate codes.

(A) Pattern separation graphs showing the average pairwise output absolute difference across GC recordings as a function of the pairwise input absolute difference, for the binwise Compactness (Left), binwise Occupancy (Middle) and overall FR (Right). Insets zoom in on the smallest values of the input-axis. Experiments using input set A and B were combined. Note that because we plot the absolute difference between two spike trains (i.e. the distance between two data points in Fig 5A) and not a measure of similarity, pattern separation corresponds to points above the identity line (dashed) and pattern convergence to points below. Error bars are SEM (not displayed in middle graph for readability). Right: input set A recordings (PΔFR) in black, input set B (B 10.5 Hz) in green. (B) The difference in dispersion (Dispersionoutput−Dispersioninput; dispersion is the mean absolute difference, i.e. red bars in Fig 5A) of binwise Compactness, binwise Occupancy or overall FR for each GC recording set from three experiments using different types of input sets: Poisson trains with 10 Hz FR and varying R (P10Hz: 102 recordings, 28 GCs), Poisson trains with R ≈ 0.75 and varying FR (PΔFR, i.e. input set A: 5 GCs), trains with 10 Hz FR and varying burstiness (B 10.5 Hz, i.e. input set B: 3 GCs). Positive values correspond to pattern separation through variations of the given spike train property (Compactness, Occupancy or FR) and negative values to pattern convergence. Asterisks denote significant difference from 0, i.e. either pattern separation or convergence (one-sample t-test, p Table 2).