Descending neurons of the hoverfly respond to pursuits of artificial targets
Data files
Sep 11, 2023 version files 21 GB
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Prism_files.zip
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RawData.zip
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README_TSDN20230911.docx
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README.md
Mar 22, 2024 version files 21 GB
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Prism_files.zip
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RawData.zip
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README_TSDN20230911.docx
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README.md
Abstract
Many animals use motion vision information to control dynamic behaviors. Predatory animals, for example, show an exquisite ability to detect rapidly moving prey, followed by pursuit and capture. Such target detection is not only used by predators but is also important in conspecific interactions, such as for male hoverflies defending their territories against conspecific intruders. Visual target detection is believed to be subserved by specialized target-tuned neurons found in a range of species, including vertebrates and arthropods. However, how these target-tuned neurons respond to actual pursuit trajectories is currently not well understood. To redress this, we recorded extracellularly from target-selective descending neurons (TSDNs) in male Eristalis tenax hoverflies. We show that they have dorso-frontal receptive fields with a preferred direction up and away from the visual midline. We reconstructed visual flow fields as experienced during pursuits of artificial targets (black beads). We recorded TSDN responses to six reconstructed pursuits and found that each neuron responded consistently at remarkably specific time points but that these time points differed between neurons. We found that the observed spike probability was correlated with the spike probability predicted from each neuron's receptive field and size tuning. Interestingly, however, the overall response rate was low, with individual neurons responding to only a small part of each reconstructed pursuit. In contrast, the TSDN population responded to substantially larger proportions of the pursuits but with lower probability. This large variation between neurons could be useful if different neurons control different parts of the behavioral output.
README: Descending neurons of the hoverfly respond to pursuits of artificial targets
We have uploaded all data associated with the paper.
Briefly, we recorded extracellularly from target selective descending neurons (TSDNs) in male Eristalis tenax hoverflies. We first analysed physiological characteristics including the receptive fields, preferred direction, size, speed, and contrast sensitivity using simple stimuli. We next reconstructed visual flow fields as experienced during pursuits of artificial targets (black beads). The reconstructed visual flow fields show more dynamic and more naturalistic target trajectories. We then recorded the responses to reconstructed target pursuits, and compared the observed response with the response predicted from the simple stimuli.
Data contents
- The “RawData” folder contains the raw data from individual TSDNs
Each subfolder in RawData is named as e.g., '211021_NO1PO1_TSDN' indicating the following;
- Recording date, which is when the TSDN data were recorded, given in the YYMMDD format
- Animal and position number, which is based on the identity number of the animal (N) and recording position (P), e.g., N01P01
- Within each subfolder you will find:
- Each electrophysiology data file, which is e.g. named 2021-10-07@09_59_20-TargetLeft-N01-P01-Trial1.mat
- (1) 2021-10-07 is the date of the recording
- (2) 09_59_20 is the time given in the hours_minutes_seconds format
- (3) TargetLeft indicates Experiment Name
- (4) N01-P01 indicates the identity number of animal (N) and recording position (P) on the recording date
- (5) Trial1 indicates trial number if applicable
- (6) Each file contains spike-sorted data as Units, together with its associated stimulus parameters such as stimulus size (Height, Width in pixel), position (Xpos, Ypos in pixel), moving speed (Velocity in pixel/s), moving direction (Direction in degree, 0: rightward, 90: upward, 180: leftward, 270: downward), Brightness, Duration (Time in frame), inter-trial duration (PauseTime in frame), Pre stimulation time (PreStimTime in frame), Post stimulation time (PostStimTime in frame) in Layer_1_Parameters
- Raw data files for the receptive field mapping with a target moving leftward are named as e.g., 2021-10-07@09_59_20-TargetLeft-N01-P01-Trial#.mat, where # specify the trial number using 20 different vertical positions.
- Raw data files for the receptive field mapping with a target moving rightward are named as e.g., 2021-10-07@09_57_40-TargetRight-N01-P01-Trial#.mat, where # specify the trial number using 20 different vertical positions.
- Raw data files for the receptive field mapping with a target moving upward are named as e.g., 2021-10-07@10_02_37-TargetUp-N01-P01-Trial#.mat, where # specify the trial number using 20 different horizontal positions.
- Raw data files for the receptive field mapping with a target moving downward are named as e.g., 2021-10-07@10_00_52-TargetDown-N01-P01-Trial#.mat, where # specify the trial number using 20 different horizontal positions.
- RFleft.mat, which contains processed data for the receptive field for a target moving leftward horizontally shown Fig. 1AB. To get this file, we used the data in 2021-10-07@09_59_20-TargetLeft-N01-P01-Trial#.mat.
- RFright.mat, which contains processed data for the receptive field for a target moving rightward horizontally shown Fig. 1B. To get this file, we used the data in 2021-10-07@09_57_40-TargetRight-N01-P01-Trial#.mat.
- RFup.mat, which contains processed data for the receptive field for the target moving upward vertically shown Fig. 1B. To get this file, we used the data in 2021-10-07@10_02_37-TargetUp-N01-P01-Trial#.mat.
- RFdown.mat, which contains processed data for the receptive field for a target moving downward vertically shown Fig. 1B. To get this file, we used the data in 2021-10-07@10_00_52-TargetDown-N01-P01-Trial#.mat.
- TargetRFmap.mat, which contains data we calculated each neuron’s average receptive field to the four directions of motion using data in RFleft.mat, RFdown.mat, RFright.mat, and RFup.mat to determine the 50% response contour shown in Fig. 1C
- Raw data files for the bar size tuning experiment are named as e.g., 2021-10-07@10_04_10-Bar_SizeTuning_LR-N01-P01-Trial#.mat, where # specify the trial number using different bar heights. The bar width was fixed at 15 pixels. We used targets scanning the screen at 900 pixels/s (55°/s) horizontally through each neuron’s receptive field. We used the neuron’s preferred horizontal direction, either rightward ‘LR’ or rightward ‘RL’ motion.
- BarSizeTuning_output.mat, which contains processed bar size tuning data that shown in Fig. 2A. To get this file, we analysed data in 2021-10-07@10_04_10-Bar_SizeTuning_LR-N01-P01-Trial#.mat.
- Raw data files for testing the response to looming stimulus are named as e.g., 2021-10-07@09_59_08-Looming_lv10-N01-P01-Trial1.mat.
- Raw data files for testing the response to looming stimulus are named as e.g., 2021-10-07@09_59_08-Looming_lv10-N01-P01-Trial1.mat. Data are used to confirm the neuron did not respond to a looming stimulus.
- Raw data files for testing the response to a circle appearance are named as e.g., 2021-05-21@14_06_54-SolidCtrl_lv10-N01-P02-Trial1.mat. Data are used to confirm the neuron did not respond to an appearing stimulus.
- Each electrophysiology data file, which is e.g. named 2021-10-07@09_59_20-TargetLeft-N01-P01-Trial1.mat
- For the neurons listed under “TSDNs recorded circle size tuning” below the subfolder will also contain:
- 2021-05-21@14_33_53-SizeTuning_Circles_Right-N01-P02-Trial#.mat, which contains raw data for testing circle size tuning, where # specify the trial number using 10 different circle size.
- CircleSizeTuning_output.mat, which contains the processed circle size data shown in Fig. 2A. This mat file stores different circle sizes (unique_size_cir.mat in pixel and height_deg_cir.mat in degree) and responses (Mean_AW_cir.mat).
- For the neurons listed under “TSDNs recorded speed size tuning using circle diameters of 4 pixels”; “TSDNs recorded speed size tuning using circle diameters of 8 pixels”; “TSDNs recorded speed size tuning using circle diameters of 15 pixels”; “TSDNs recorded speed size tuning using circle diameters of 25 pixels”; “TSDNs recorded speed size tuning using circle diameters of 50 pixels” below the subfolder will also contain:
- 2021-05-21@14_35_25-VelocityTuning_Circles4_LR-N01-P02-Trial#.mat, which contains raw data for testing circle speed tuning, where # specifies the trial number using different speeds. Circle size was specified as e.g., ‘Circles4’.
- CircleSpeedTuning_output#.mat, which contains the processed speed tuning data using a specified circle size as # (either 4, 8, 15, 25, or 50). This mat file stores information about different circle speed (unique_speed.mat in pixel/s and speed_deg.mat in degree/s), responses (Mean_AW.mat), spontaneous responses (spont.mat), target size (tsize), cm-to-pixel ratio of the screen (cmPerpx.m) and screen distance to the animal (screen_dist.m in cm). Processed data are shown in Fig. 2B.
- For the neurons listed under “TSDNs recorded contrast tuning” below the subfolder will also contain:
- 2021-05-21@14_42_51-ContrastTuning_TargetGreybg-N01-P02-Trial#.mat, which contains raw data for testing contrast tuning, where # specify the trial number using different Weber contrast.
- CircleContrastTuning_output.mat, which contains the processed contrast tuning data. This mat file stores information about stimulus contrast (unique_contrast.mat), responses (Mean_AW.mat), and spontaneous responses (spont.mat). Processed data are shown in Fig. 2C.
- For the neurons listed under “TSDNs recorded responses to the reconstructed pursuits” below the subfolder will also contain:
- 2021-05-21@14_28_00-360_2Dsm-N01-P02-Trial1-495.mat, which contains raw data for testing responses to the reconstructed pursuits. The yellow highlighted unique trajectory number e.g., 3*60* corresponding to trajectories i in Figure 5. The trajectories ii-vi are referred to 524, 574, 404, 348, or 427.
- 2021-05-21@14_28_11-Long_short_LR-N01-P02-Trial#.mat, which contains raw data for testing responses to a target in trial1 or bar in trial2. This is used to confirm the neuron responds to only a target stimulus.
- Observed_spk_probability_data.mat, which contains observed spike probability data for each reconstructed trajectory. This file contains the information below
- Observed_spk_probability_trackfiles: Trajectory number 3*60, 524, 574, 404, 348, and 427* corresponding to trajectories i -vi.
- Observed_spk_probability: Observed spike probability of the TSDN for each trajectory
- ifi_all: Interframe interval for each trajectory in second
- PD_all: Preferred direction of the TSDN
- RFx_center: Receptive field center’s horizontal location
- RFy_center: Receptive field center’s vertical location
- Observed_spk_probability2: Observed spike probability of the TSDN for all trajectories to analyze together
- spikesN_frameAll: Observed numbers at each frame for each trajectory
- Probability_observed_predicted_each_neuron.mat, which contains the observed spike probability and predicted spike probability from the model calculation. This file contains the information below
- Observed_spk_prob_indi: Observed spike probability of the TSDN for each trajectory
- Predicted_spk_prob_Pos_Dir_Size_indi: Predicted spike probability, expected from the receptive field, directionality, and size tuning of the TSDN for each trajectory
- Predicted_spk_prob_Pos_Dir_Size1: Predicted spike probability, expected from the receptive field, directionality, and size tuning of the TSDN across trajectory
- Observed_spk_prob1: Observed spike probability of the TSDN for all trajectories to analyze together
- Observed_spk2_probAll: Observed spike probability of the TSDN for all trajectories to analyze together
- Observed_spk2_prob_indi: Observed spike probability of all TSDNs for each trajectory
- Observed_spk2_prob1: Observed spike probability of the TSDN for all trajectories to analyze together
- RFx_centerAll: Receptive field center’s horizontal location of all TSDNs
- RFx_center_indi: Receptive field center’s horizontal location of each TSDN
- RFy_centerAll: Receptive field center’s vertical location of all TSDNs
- RFy_center_indi: Receptive field center’s vertical location of each TSDN
- PD_allAll: Preferred direction of all TSDN
- PD_all_indi: Preferred direction of the TSDN
- Corrcoef_indiNeuron.mat, which contains correlation coefficients between observed spike probability and predicted spike probability
- c3_each_track: Pearson correlation coefficient for each trajectory
- TargetRF_size.mat, which contains the RF map scaling information for the model calculation produced by ‘SpikePrediction_model_RFs.m’
- InterpRFs: Interpolated each neuron’s receptive field shape for four directions of motion at 1° resolution
- InterpRFs_size_scale: Normalized neuron’s receptive field to a maximum of 1, dividing InterpRFs by spikes_3deg
- xi: Horizontal axis of 100 x 100 grid of the receptive field maps
- yi: Vertical axis of 100 x 100 grid of the receptive field maps
- Size_edges: Unique sizes of interpolated size response function
- SizeTuningData: Interpolated size response function to bar stimuli
- spikes_3deg: Spike number of the neuron to 3-degree target moving in the preferred horizontal direction
TSDNs recorded circle size tuning
- '210521_NO1PO2_TSDN'
- '210524_NO1PO2_TSDN'
- '210819_NO2PO2_TSDN'
- '210929_NO1PO1_TSDN'
- '211101_N01P01_TSDN'
- '210930_N02P01_TSDN'
- '210927_N02P01_TSDN'
- '210914_N01P01_TSDN'
- '210831_N03P02_TSDN'
- '210423_N02P01_TSDN'
- '210423_N01P01_TSDN'
- '210324_N03P01_TSDN'
- '210224_N02P02_TSDN'
- '210224_N01P02_TSDN'
TSDNs recorded speed size tuning using circle diameters of 4 pixels
- '210521_NO1PO2_TSDN'
- '210524_NO1PO2_TSDN'
- '210819_NO2PO2_TSDN'
- '210929_NO1PO1_TSDN'
- '210914_N01P01_TSDN'
- '210831_N03P02_TSDN'
- '210423_N02P01_TSDN'
- '210423_N01P01_TSDN'
- '210324_N03P01_TSDN'
- '210929_NO1PO1_TSDN'
- '211101_N01P01_TSDN'
- '210930_N02P01_TSDN'
- '210927_N02P01_TSDN'
- '211107_N03P02_TSDN'
- '210929_N02P01_TSDN'
- '211007_N01P01_TSDN'
TSDNs recorded speed size tuning using circle diameters of 15 pixels
- '210521_NO1PO2_TSDN'
- '210524_NO1PO2_TSDN'
- '210819_NO2PO2_TSDN'
- '220823_NO2PO1_TSDN'
- '220826_NO2PO1_TSDN'
- '220901_NO4PO1_TSDN'
- '220902_NO1PO1_TSDN'
- '220905_NO1PO1_TSDN'
- '220905_NO3PO1_TSDN'
- '220905_NO3PO2_TSDN'
- '220906_NO1PO1_TSDN'
- '220906_NO2PO1_TSDN'
- '220909_NO3PO1_TSDN'
- '220909_NO3PO2_TSDN'
TSDNs recorded speed size tuning using circle diameters of 25 pixels
- '210929_NO1PO1_TSDN'
- '211101_N01P01_TSDN'
- '210930_N02P01_TSDN'
- '210927_N02P01_TSDN'
- '211107_N03P02_TSDN'
- '210929_N02P01_TSDN'
- '211007_N01P01_TSDN'
TSDNs recorded speed size tuning using circle diameters of 50 pixels
- '210521_NO1PO2_TSDN'
- '210524_NO1PO2_TSDN'
- '210819_NO2PO2_TSDN'
- '210914_N01P01_TSDN'
- '210423_N02P01_TSDN'
- '210423_N01P01_TSDN'
- '210324_N03P01_TSDN'
- '210224_N02P02_TSDN'
- '210224_N01P02_TSDN'
TSDNs recorded contrast tuning
- '210521_NO1PO2_TSDN'
- '210524_NO1PO2_TSDN'
- '210819_NO2PO2_TSDN'
- '210929_NO1PO1_TSDN'
- '210930_N02P01_TSDN'
- '210927_N02P01_TSDN'
- '210914_N01P01_TSDN'
- '210423_N02P01_TSDN'
- '210324_N03P01_TSDN'
- '210224_N02P02_TSDN'
- '210224_N01P02_TSDN'
- '210212_N01P02_TSDN'
- '211007_N01P01_TSDN'
TSDNs recorded responses to the reconstructed pursuits
- '211021_NO1PO1_TSDN'
- '210429_NO1PO1_TSDN'
- '210521_NO1PO2_TSDN'
- '210524_NO1PO2_TSDN'
- '210819_NO2PO2_TSDN'
- '210929_NO1PO1_TSDN'
- '211101_NO1PO2_TSDN'
- '211101_NO2PO1_TSDN'
- '211213_NO1PO1_TSDN'
- '211213_NO1PO2_TSDN'
- '211214_NO1PO1_TSDN'
- '220823_NO2PO1_TSDN'
- '220826_NO2PO1_TSDN'
- '220901_NO3PO1_TSDN'
- '220901_NO3PO2_TSDN'
- '220901_NO4PO1_TSDN'
- '220902_NO1PO1_TSDN'
- '220905_NO1PO1_TSDN'
- '220905_NO2PO2_TSDN'
- '220905_NO3PO1_TSDN'
- '220905_NO3PO2_TSDN'
- '220906_NO1PO1_TSDN'
- '220906_NO2PO1_TSDN'
- '220909_NO1PO1_TSDN'
- '220909_NO2PO2_TSDN'
- '220909_NO3PO1_TSDN'
- '220909_NO3PO2_TSDN'
The “Prism_files” folder contains the processed data shown in the figures. They are labeled according to the corresponding figure number as given in the paper.
Methods
Please see the README file and methods in the paper.
Usage notes
We used PRISM 9.0 and Matlab 2019b.