We have also considered partitioning between nonspatial criteria such as IN versus OUT of a cell’s episode-field in the wheel (which is related to time rather than to space) or future maze-arm-running direction (next-left versus next-right runs in the wheel) and calculated the corresponding “discrimination” information content relating the events probabilities and their associated relative firing rates. Therefore, in our analysis of inside (IN-PF) versus outside (OUT-PF) place field discrimination, spatial information content is related to the probability that the animal is either inside or outside the cell’s place field when

this given cell is firing an action potential. Osimertinib manufacturer The same applies for the discrimination between Everolimus two distinct episode fields in the wheel, between two distinct place fields/episode fields, or between next-left versus next-right runs in the wheel. The net gain of information from taking TPSM phase into account was calculated for each cell by difference between the mean information content carried by the 20% of the total number of spikes discharged by the cell near its place field (or episode field or next-left/next-right runs in the wheel) preferred TPSM-phase (phase IN) and the average information content of the same number of spikes taken at systematically shifted TPSM-phases (all phases: 20 spike subsets, each discharged

around a distinct TPSM phase determined as an incremental π/10 systematic phase offset relative to the preferred phase). Therefore, we have quantified how much information (in bits per spike) was added to the spikes discharged by an individual

cell by taking into account the TPSM phase at which each of these spikes were fired. The paired Student’s t test was used for statistical comparisons (complete numerical values for the statistic are provided as Table S2). Unless stated otherwise, values are presented as mean ± SEM. This work was performed thanks to the following funding sources: INSERM (X.L.), FRM (X.L.), CNRS (X.L., J.O.), Région Aquitaine (X.L.), ENI-Net (X.L.), ANR (X.L. and C.M.). We wish to thank E. Pastalkova and G. Buzsáki for maze and unless wheel data, J. Csicsvari, K.D. Harris, L. Hazan and M. Zugaro for analysis software, Partha Mitra for advices regarding theta power analysis tools, John Finlayson for editing the manuscript, Thomas Leinekugel for Matlab programming, John Finlayson for thoroughly editing the manuscript, Anna Beyeler, Michele Pignatelli, Yannick Jeantet, and Thibault Maviel for useful comments and discussion. C.M. and X.L. designed the study, performed analysis, and wrote the manuscript; X.L. and H.H. performed experimental recordings, J.O. participated in clustering, Y.Y. provided support in analysis and funding of the project.