Data from: Exponential history integration with diverse temporal scales in retrosplenial cortex supports hyperbolic behavior

Analysis Code Code written in Python available in the linked Zenodo repository. Behavior data Data was recorded during mouse in vivo imaging or inactivation sessions. Behavior data is saved in Python pickle files in the pandas dataframe format.a: choice type for each trial (Right choice, Left choice, Alarm, Miss) = (1,2,3,4)R: reward outcome type for each trial. (Reward delivered, Reward not delivered) = (1,0) Imaging data Processed two-photon calcium imaging data, aligned to trial ‘ready’ period or ‘choice’ period. Saved as a netcdf file in the xarray dataset format, bundling neural activity for all cells recorded in a session with that session’s behavior metrics. spks: deconvolved estimate spike rate, aligned to ready cue onset. First data point corresponds to 2sec before ready cue onset. Last data point corresponds to 10sec after ready cue onset.z_spks: zscore normalized spks, each cell zscored over the entire session. Inactivation data Behavior data from the inactivation sessions includes variable ‘a’ and ‘R’ as well as the inactivation condition ‘Opto’ and metadata associated with the inactivation parameters. a: choice type for each trial (Right choice, Left choice, Alarm, Miss) = (1,2,3,4)R: reward outcome type for each trial. (Reward delivered, Reward not delivered) = (1,0)Opto: inactivation status for the trial. (cortical inactivation, control inactivation) = (1,0)Inac Type: trial-aligned timing of inactivation, either aligned to the 'ready-answer' periods, the 'intertrial interval', or the both. String = ('ra', 'iti', '0i1ra')Area: the area targeted for inactivation in that session Analysis outputs All of the outputs associated with the analysis are located in the respective directories. Reference the Jupyter notebook files for the specific analysis performed and the generation of the figures from the output data.