Post-processed neural and behavioral data class with reward-relative cells identified

This dataset contains 2 pickled Python files containing the post-processed data used for: Sosa, Plitt, Giocomo, 2025. A flexible hippocampal population code for experience relative to reward. Nature Neuroscience. https://doi.org/10.1038/s41593-025-01985-4
Hippocampal neurons were recorded in dorsal CA1 using 2-photon calcium imaging and synchronized with virtual reality behavior in head-fixed mice. This data is referred to as "post-processed" because shuffles have already been run to identify cells as "reward-relative" or not; therefore, the cell ID labels in this dataset will allow exact replication of any figures in the paper that do not require an additional shuffle of the data. The pickle or dill package in Python is required to load this dataset.
See accompanying dataset: Pre-processed neural and behavioral data, including raw fluorescence.
See the code base on Github for usage and additional documentation.

File name format: m[mouse-number-range]_expdays[list-of-day-numbers]_multiDayData_dff_[date saved, yyyymm]

Each pickle file is a Python dictionary, either for (1) only the experimental days where a reward zone location was switched on a virtual linear track (3-5-7-8-10-12-14) or (2) for all days, including days where the reward zone remained in the same location (1...14). In pickle (2), the data on the switch days are identical to pickle (1) -- we have provided both options to allow users to download a smaller file size if they are only interested in the "switch" days. Each entry of the dictionary corresponds to a class object for a given experimental day indexed as [3, 5, 7, 8, 10, 12, 14], for example, corresponding to the day number.

Below are the most relevant attributes of the class for analyses in the paper. Additional attributes are explained in the dayData.py docstring on the Github. Values before the '--' are defaults.
self.anim_list: list of mouse IDs included in this dayself.place_cell_logical: 'or' -- cells were classified as place cells by having significant spatial information in the trials before OR after the reward switchself.force_two_sets: True -- trials were split into "set 0" before the reward switch, and "set 1" after the reward switch. In animals without a reward switch, "set 0" and "set 1" correspond to the 1st and 2nd half of trials, respectivelyself.ts_key: 'dff' -- timeseries data type (dF/F) used to find place cell peaksself.use_speed_thr: True -- whether a running speed threshold was used to quantify neural activityself.speed_thr: 2 -- the speed threshold used, in cm/sself.exclude_int: True -- whether putative interneurons were excluded from analysesself.int_thresh: 0.5 -- speed correlation threshold to identify putative interneuronsself.int_method: 'speed' -- method of finding putative interneuronsself.reward_dist_exclusive: 50 -- distance in cm to exclude cells "near" rewardself.reward_dist_inclusive: 50 -- distance in cm to include cells as "near" rewardself.bin_size: 10 -- linear bin size (cm) for quantifying spatial activityself.sigma: 1 -- Gaussian s.d. in bins for smoothingself.smooth: False -- whether to smooth binned data for finding place cell peaksself.impute_NaNs: True -- whether to impute NaN bins in spatial activity matricesself.sim_method: 'correlation' -- trial-by-trial similarity matrix method: 'cosine_sim' or 'correlation'self.lick_correction_thr: 0.35 -- threshold to detect capacitive sensor errors and set trial licking to NaN self.is_switch: whether each animal had a reward switchself.anim_tag: string of animal ID numbersself.trial_dict: dictionary of booleans identifying each trial as in "set 0" or "set 1"self.rzone_pos: [start, stop] position of each reward zone (cm)self.rzone_by_trial: same as above but for each trialself.rzone_label: label of each reward zone (e.g. 'A', 'B')self.activity_matrix: spatially-binned neural activity of type self.ts_key (trials x position bins x neurons)self.events: original spatially-binned deconvolved events (trials x position bins x neurons) (no speed threshold applied)self.place_cell_masks: booleans identifying which cells are place cells in each trial setself.SI: spatial information for each cell in each trial setself.overall_place_cell_masks: single boolean identifying which cells are place cells according to self.place_cell_logicalself.peaks: spatial bin center of peak activity for each cell in each trial setself.field_dict: dictionary of place field properties for each cellself.plane_per_cell: imaging plane of each cell (all zeros if only a single plane was imaged, otherwise 0 or 1 if two planes were imaged)self.is_int: boolean, whether each cell is a putative interneuronself.is_reward_cell: boolean, whether each cell has a peak within 50 cm of both reward zone startsself.is_end_cell: boolean, whether each cell has a peak in the first or last spatial bin of the trackself.is_track_cell: boolean, whether each cell's peak stays within 50 cm of itself from trial set 0 to trial set 1self.sim_mat: trial-by-trial similarity matrix for place cells, licking, and speedself.in_vs_out_lickratio: ratio of lick rate in the anticipatory zone vs. everywhere outside the anticipatory and reward zonesself.lickpos_std: standard deviation of licking positionself.lick_mat: matrix of lick rate in each spatial bin (trials x position bins)self.cell_class: dictionary containing booleans of which cells have remapping types classified as "track", "disappear", "appear", "reward", or "nonreward_remap", where:'track' = track-relative'disappear' = disappearing'appear' = appearing'reward' = remap near reward (firing peak ≤50 cm from both reward zone starts), including reward-relative 'nonreward_remap' = remap far from reward (>50 cm from reward zone start), including reward-relativeSee Fig. 2 notebook and code docstrings for more details.self.pos_bin_centers: position bin centersself.dist_btwn_rel_null: distance between spatial firing peaks relative to reward before the switch and the "random remapping" shuffle after the switch (radians)self.dist_btwn_rel_peaks: distance between spatial firing peaks relative to reward before vs. after the switch (radians)self.reward_rel_cell_ids: integer cell indices that were identified as reward-relative after application of all criteriaself.xcorr_above_shuf: lag, in spatial bins, of the above-shuffle maximum of the cross-correlation used to confirm cells as reward-relative (computed for all cells; NaNs indicate that the xcorr did not exceed shuffle)self.reward_rel_dist_along_unity: circular mean of pre-switch and post-switch spatial firing peak position relative to reward (radians)self.rel_peaks: spatial firing peak position relative to reward in each trial set (radians)self.rel_null: spatial firing peak position relative to reward, for the random-remapping shuffle post-switch (radians)self.circ_licks: spatially-binned licking, in circular coordinates relative to reward (trials x position bins)self.circ_speed: spatially-binned speed, in circular coordinates relative to reward (trials x position bins)self.circ_map: mean spatially-binned neural activity within each trial set, of type self.ts_key, in circular coordinates relative to rewardself.circ_trial_matrix: spatially-binned neural activity of type self.ts_key, in circular coordinates relative to reward (trials x position bins x neurons)self.circ_rel_stats_across_an: metadata across the "switch" animals:'include_ans': list of "switch" animal names'rdist_to_rad_inc': self.reward_dist_inclusive converted to radians'rdist_to_rad_exc': self.reward_dist_exclusive converted to radians'min_pos': minimum position bin used'max_pos': maximum position bin used'hist_bin_centers': bin centers used for spatial binning