Single granule cells exhibit pattern separation on millisecond to second time scales using different codes.

(A-C) Pattern separation/convergence assessed at different time scales and using different measures of similarity (S). Top: pairwise Soutput (average across recording sets) as a function of pairwise Sinput, measured with different binning windows τw. Solid curves are linear fits. Each color corresponds to a different τw ranging from 10 ms to 500 ms. Bottom: Effect of τw on the effective decorrelation (Sinput−Soutput), interpolated from the linear regressions. Note that as τw increases, pattern separation through decorrelation (R) or orthogonalization (NDP) becomes weaker while it becomes stronger through scaling (SF). (D) Similarity between spike trains is here assessed with the binless SPIKE metric, directly using spike times. Left: example of two input spike trains associated with two output spike trains from a GC recording set, and the corresponding distances D(t) between spike trains. D(t) can then be integrated over time to give a single value D. Middle: example of 55x55 matrix of SPIKE similarity (1-D) between all spike trains of an example recording set. 0 means that spikes of two trains never happened close in time, and 1 that they were perfectly synchronous. The output SPIKE similarity (SPIKEoutput) is defined as the average similarity excluding comparisons between spike train from the same parent input train (i.e. average of the 10 pairwise SPIKEoutput). Right: SPIKEoutput of the same 102 GC recordings as in Figs 1–3, as a function of SPIKEinput, fitted with a parabola (red line). Most data points are below the dashed identity line indicating that output spike trains are less similar than inputs. The average SPIKEinput-SPIKEoutput is significantly above 0 for all input sets except the two most dissimilar (SPIKEinput = 0.74, 0.78) (one-sample T-tests, p < 0.05).