Behavioral and 2P Calcium Imaging Data for "A neural circuit architecture for rapid learning in goal-directed navigation"

Behavioral and 2P calcium imaging data for the following paper:

Dan C, Hulse BK, Kappagantula R, +Jayaraman V & +Hermundstad AM (2024), A neural circuit architecture for rapid learning in goal-directed navigation.

Data files for different groups of flies:

• Preproc_*_FlightBehaviorData.mat: preprocessed behavioral data
• Preproc_*_CAImaging_FlightBehaviorData.mat: preprocessed behavioral data and raw calcium imaging data
• Segmented_*_FlightBehaviorData.mat: processed behavioral data segmented into fixations and saccades
• ProcJumpData_*.mat: processed Calcium imaging data to track jumps in the compass heading over time

File descriptors:

• Punishment ('Laser'; 'NoLaser'): indicates whether or not flies underwent laser training
• Genotype ('WT', 'SS00090 Parent/Kir', 'SS00096 Parent/Kir', '60D05')

File organization:

• Each file contains MATLAB data structures indexed by fly i and trial j
• PreProcData*.fly(i).trial(j) contains the following fields:
• • x: frame position of visual scene in arena (can take values from 0 to 95, which span 360deg)
  • wba: difference in wing-beat amplitude between left and right wings
  • wbf: wingbeat frequency
  • time: time in seconds
  • laser: binary number indicating whether laser punishment was delivered
  • puff: binary number indicating whether an airpuff was delivered
  • trialType: 'naive', 'train', or 'probe'
  • bgsubF: background-subtracted fluorescence (only for 60D05 flies)
• SegData*.fly(i).trial(j) contains the following fields:
• • saccades: structure containing properties of saccades
  • fixations: structure containing properties of fixations
  • drift: structure containing properties of drift periods that were not classifies as either fixations or saccades
  • PI: performance score for trial
  • PIinds: indices of timepoints used to compute PI scores and other behavioral measures
  • nlaser: number of timepoints at which laser punishment was delivered.
  • residency: number of timepoints spent at each of 48 frame positions (i.e., collapsed across the two-fold symmetry of the visual scene)
  • shifted: data structure containing data aligned to the preferred heading of each fly, defined over the 48 frame positions in the collapsed span of the arena
  • residencyFull: number of timepoints spent at each of 96 frame positions
  • shiftedFull: data structure containing data aligned to the preferred heading of each fly, defined over all 96 frame positions
• tSeriesProcAll(i).jumpData(j) contains the following fields:
• • dffMat: fluorescence for 32 wedges in the ellipsoid body, tracked across time
  • arena: orientation of visual scene in arena, defined from -pi to pi radians
  • correctedPVA_EB: original population vector average (PVA) of activity in the EB after correcting for multiple offsets
  • origPVA_EB: original population vector average (PVA) of activity in the EB, without correcting for multiple offsets
  • PVA_EB_strength: strength of corrected PVA
  • FRmaps.FRMapHeading: average fluorescence in each of 32 wedges of the EB for each of 48 orientations of the visual scene (i.e., collapsed across the two-fold symmetry of the visual scene)
  • bumpjumps: structure containing properties of offset between the compass bump and the visual scene
  • the following fields are copied over from the data structures above:
  • saccades: structure containing properties of saccades (copied from SegData*.fly(i).trial(j))
  • fixations: structure containing properties of fixations (copied from SegData*.fly(i).trial(j))
  • setpoint: preferred arena heading, defined from 0 to 95 pixels
  • PIinds: indices of timepoints used to compute PI scores and other behavioral measures
  • x: frame position of visual scene in arena (can take values from 0 to 95, which span 360deg)
  • wba: difference in wing-beat amplitude between left and right wings
  • wbf: wingbeat frequency
  • time: time in seconds
  • trialType: 'naive', 'train', or 'probe'

Associated code:

• Code for FPGA camera-based wingbeat analyzer is available at: https://github.com/ChuntaoDan/FPGA_WingbeatAnalyzer (DOI: 10.5281/zenodo.11060747)
• Code for all behavioral analyses, calcium imaging analyses, modeling, and plotting is available at https://github.com/HermundstadLab/flyVisualLearning (DOI: 10.5281/zenodo.11062515)