This README.txt file was generated on 2021-04-27 by Luis M. Franco GENERAL INFORMATION 1. Title of Dataset: A Distributed Circuit for Associating Environmental Context to Motor Choice in Retrosplenial Cortex 2. Author Information A. Principal Investigator Contact Information Name: Michael J. Goard Institution: University of California Santa Barbara (UCSB) Address: 6131 Biological Science Building II, University of California Santa Barbara (UCSB), Santa Barbara, CA 93106-5060 Email: michael.goard@lifesci.ucsb.edu B. Associate or Co-investigator Contact Information Name: Luis M. Franco Institution: University of California Santa Barbara (UCSB) Address: 6131 Biological Science Building II, University of California Santa Barbara (UCSB), Santa Barbara, CA 93106-5060 Email: luis.franco@lifesci.ucsb.edu 3. Date of data collection: behaviorData.mat ---> 2018-08-01 to 2020-02-29 wideFieldData.mat ---> 2019-08-09 to 2020-02-19 twoPhotonData.mat ---> 2018-09-13 to 2019-12-03 4. Geographic location of data collection: Santa Barbara, CA, USA 5. Information about funding sources that supported the collection of the data: This work was supported by the Harvey Karp Discovery Award (L.M.F.) and UC MEXUS-CONACYT Postdoctoral Fellowship (L.M.F.), NIH R00MH104259 (M.J.G.), NSF 1707287 (M.J.G.), the Hellman Fellows Fund (M.J.G.), the Larry L. Hillblom Foundation (M.J.G.), and the Whitehall Foundation (M.J.G.). SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: Please cite these data as: 2. Links to publications that cite or use the data: https://www.biorxiv.org/content/10.1101/2020.12.20.423684v1.full 3. Links to other publicly accessible locations of the data: No other publicly accessible locations. 4. Links/relationships to ancillary data sets: Not available. 5. Was data derived from another source? No. 6. Recommended citation for this dataset: Not available yet. DATA & FILE OVERVIEW 1. File List: behaviorData.mat wideFieldData.mat twoPhotonData.mat 2. Relationship between files, if important: different datasets from different experiments of the same project. 3. Additional related data collected that was not included in the current data package: No additional data. 4. Are there multiple versions of the dataset? No. METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: https://www.biorxiv.org/content/10.1101/2020.12.20.423684v1.full 2. Methods for processing the data: https://www.biorxiv.org/content/10.1101/2020.12.20.423684v1.full 3. Instrument- or software-specific information needed to interpret the data: Data are saved as .mat files that can be read in Matlab. 4. Standards and calibration information, if appropriate: Not available. 5. Environmental/experimental conditions: For a description of experimental methodology: https://www.biorxiv.org/content/10.1101/2020.12.20.423684v1.full. 6. Describe any quality-assurance procedures performed on the data: https://www.biorxiv.org/content/10.1101/2020.12.20.423684v1.full. 7. People involved with sample collection, processing, analysis and/or submission: Luis M. Franco and Michael J. Goard DATA-SPECIFIC INFORMATION FOR: behaviorData.mat This is a .mat file containing a structure array named behaviorData with fields: first5DaysOfTraining: [1×18 double] ---> behavioral performance of 18 mice during first 5 days of training last5DaysOfTraining: [1×18 double] ---> behavioral performance of 18 mice during last 5 days of training wideFieldImaging: [1×4 double] ---> behavioral performance of 4 mice during wide-field imaging experiments twoPhotonImaging: [1x10 double] ---> behavioral performance of 10 mice during two-photon imaging experiments optogenetics: [12×2 double] ---> behavioral performance of 4 mice expressing ArchT during optogenetics experiments 1. 12 experimental sessions: 4 mice, 3 sessions each 2. LED On/Off: 1. Off, 2. On optogeneticsControl: [6×2 double] ---> behavioral performance of 2 mice not expressing ArchT during wide-field imaging experiments 1. 6 experimental sessions: 2 mice, 3 sessions each 2. LED On/Off: 1. Off, 2. On closedLoop: [1x6 double] ---> behavioral performance of 6 mice during closed-loop experiments DATA-SPECIFIC INFORMATION FOR: wideFieldData.mat This is a .mat file containing a structure array named cortexData with fields: LYResponses: [400×400×90 double] ---> average calcium activity across 20 experimental sessions (DFF) for left-yellow trials 1. Size of imaging field in Y (pixels) 2. Size of imaging field in X (pixels) 3. 90 time bins (acquired at 10 Hz) LBResponses: [400×400×90 double] ---> average calcium activity across 20 experimental sessions (DFF) for left-blue trials 1. Size of imaging field in Y (pixels) 2. Size of imaging field in X (pixels) 3. 90 time bins (acquired at 10 Hz) RYResponses: [400×400×90 double] ---> average calcium activity across 20 experimental sessions (DFF) for right-yellow trials 1. Size of imaging field in Y (pixels) 2. Size of imaging field in X (pixels) 3. 90 time bins (acquired at 10 Hz) RBResponses: [400×400×90 double] ---> average calcium activity across 20 experimental sessions (DFF) for right-blue trials 1. Size of imaging field in Y (pixels) 2. Size of imaging field in X (pixels) 3. 90 time bins (acquired at 10 Hz) DATA-SPECIFIC INFORMATION FOR: twoPhotonData.mat This is a .mat file containing a structure array named cellData with fields: cellResponses: [7770×90×56×4 double] ---> calcium activity in individual neurons (DFF) 1. 7770 cells 2. 90 time bins (acquired at 10 Hz) 3. Trials (padded with NaNs for missing data) 4. Trial type: 1. Left-Yellow, 2. Left-Blue, 3. Right-Yellow, 4. Right-Blue cellsPerSession: [29×1 double] ---> number of cells in each of the 29 experimental sessions reliableCells: [5194×1 double] ---> indices of the 5194 cells with reliable responses cellCoordinates: [7770×2 double] ---> location of the 7770 recorded cells 1. 7770 cells 2. Coordinates in X and Y previousDecisions: [100x6x2x4x29 double] ---> context-decision pairs for previous decisions (helpful to track history-dependent encoding of task variables) 1. 100 trials (padded with NaNs for missing data) 2. Trials back: 1. Current trial, 2. Current trial - 1, 3. Current trial - 2, 4. Current trial - 3, 5. Current trial - 4, 6. Current trial - 5 3. Context-Decision: 1. Context: 0=Yellow, 1=Blue; 2. Response: 0=Left, 1=Right 4. Trial type: 1. Left-Yellow, 2. Left-Blue, 3. Right-Yellow, 4. Right-Blue 5. Experimental session TDR: [5×90×4 double] ---> targeted dimensionality reduction (a.u.) 1. Dimensions: 1. Activity, 2. Motor, 3. Context, 4. Reward, 5. Interactions 2. 90 time bins (acquired at 10 Hz) 3. Trial type: 1. Left-Yellow, 2. Left-Blue, 3. Right-Yellow, 4. Right-Blue bootstrappedTDR: [5×90×4×1000 double] ---> bootstrapped targeted dimensionality reduction (a.u.) 1. Dimensions: 1. Activity, 2. Motor, 3. Context, 4. Reward, 5. Interactions 2. 90 time bins (acquired at 10 Hz) 3. Trial type: 1. Left-Yellow, 2. Left-Blue, 3. Right-Yellow, 4. Right-Blue 4. 1000 bootstrap iterations singleCellSVMPerformance: [5194×90×100×3 double] ---> performance of a support vector machine decoder (50% trials for training the model, 50% trials for testing the model) 1. 5194 Cells 2. 90 time bins (acquired at 10 Hz) 3. 100 iterations 4. Variable: 1. Context, 2. Motor, 3. Outcome mapsPerCellType: [500×500×90×6 double] ---> maps for the performance of a support vector machine decoder for individual cells 1. Size of the map in Y 2. Size of the map in X 3. 90 time bins (acquired at 10 Hz) 4. Context, motor and outcome preference: 1. Yellow, 2. Blue, 3. Left, 4. Right, 5. Reward, 6. No reward allCellsSVMPerformance: [90×100×3×6 double] ---> performance of a support vector machine decoder on all cells 1. 90 time bins (acquired at 10 Hz) 2. 100 iterations 3. Variable: 1. Context, 2. Motor, 3. Outcome 4. Trials back: 1. Current trial, 2. Current trial - 1, 3. Current trial - 2, 4. Current trial - 3, 5. Current trial - 4, 6. Current trial - 5 clusterSVMPerformance: [90x100x11x3x9 double] ---> performance of a support vector machine decoder on clusters of cells 1. 90 time bins (acquired at 10 Hz) 2. 100 iterations 3. Number of RSC subdivisions (clusters): 3 to 11. 4. Variable: 1. Context, 2. Motor, 3. Outcome 5. Number of clusters: 1. 3 clusters, 2. 4 clusters, 3. 5 clusters, 4. 6 clusters, 5. 7 clusters, 6. 8 clusters, 7. 9 clusters, 8. 10 clusters, 9. 11 clusters resampledClusterSVMPerformance: [90×100×3×3 double] ---> performance of a support vector machine decoder on clusters of cells. Same as clusterSVMPerformance for 3 clusters, but neurons had been resampled to use only 1000 neurons per iteration. 1. 90 time bins (acquired at 10 Hz) 2. 100 iterations 3. 3 clusters 4. Variable: 1. Context, 2. Motor, 3. Outcome hemisphereSVMPerformance: [90×100×2×3 double] ---> performance of a support vector machine decoder on cells from each hemisphere 1. 90 time bins (acquired at 10 Hz) 2. 100 iterations 3. Hemispheres: 1. Left, 2. Right 4. Variable: 1. Context, 2. Motor, 3. Outcome hemisphereClusterSVMPerformance: [90×100×6×3 double] ---> performance of a support vector machine decoder on clusters of cells from each hemisphere by separate 1. 90 time bins (acquired at 10 Hz) 2. 100 iterations 3. Hemisphere clusters: 1. Left hemisphere posterior RSC, 2. Left hemisphere medial RSC, 3. Left hemisphere anterior RSC, 4. Right hemisphere posterior RSC, 5. Right hemisphere medial RSC, 6. Right hemisphere anterior RSC 4. Variable: 1. Context, 2. Motor, 3. Outcome