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README.md

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File structure

data/
  sensorium/
    dynamic29156-11-10-Video-8744edeac3b4d1ce16b680916b5267ce.zip
    dynamic29228-2-10-Video-8744edeac3b4d1ce16b680916b5267ce.zip
    ...
  rochefort-lab/
    VIPcre232_FOV1_day1/
    VIPcre232_FOV1_day2/
    ...
  compute_metadata.py
  download_GIN.py
  README.md
  save_response.py

Helper files and scripts

  • Please check viv1t/data/metadata/README.md for additional metadata that were extracted from the dataset.
  • assign_video_ids.py is a helper script to iterate all visual stimuli in each mouse directory and assign a unique video ID to each unique visual stimulus: python assign_video_ids.py --data_dir=sensorium

Stimulus information

  • We assign each stimulus type an ID with the following mapping, see assign_stimulus_ids.py for implementation. 
    STIMULUS_TYPES = {
    0: "movie",
    1: "directional pink noise",
    2: "gaussian dots",
    3: "random dot kinematogram",
    4: "drifting gabor",
    5: "image",
}
Mouse live_bonus final_bonus
A directional pink noise gaussian dots, random dot kinematogram
B drifting gabor directional pink noise, image
C random dot kinematogram drifting gabor, image
D image directional pink noise, gaussian dots
E gaussian dots random dot kinematogram, drifting gabor
F directional pink noise gaussian dots, random dot kinematogram
G drifting gabor random dot kinematogram, image
H gaussian dots directional pink noise, image
I random dot kinematogram gaussian dots, drifting gabor
J image directional pink noise, drifting gabor

Drifting Gabor information

  • We extracted the drifting Gabor parameters (orientation, wavelength and frequency) to estimate the tuning properties of the recorded and predicted responses, see estimate_gabor_features.py for implementation.

Sensorium 2023 datasets

  • Original dataset gin.g-node.org/pollytur/Sensorium2023Data with Mouse A, B, C, D, E. 
    dynamic29156-11-10-Video-8744edeac3b4d1ce16b680916b5267ce.zip # mouse A
dynamic29228-2-10-Video-8744edeac3b4d1ce16b680916b5267ce.zip # mouse B
dynamic29234-6-9-Video-8744edeac3b4d1ce16b680916b5267ce.zip # mouse C
dynamic29513-3-5-Video-8744edeac3b4d1ce16b680916b5267ce.zip # mouse D
dynamic29514-2-9-Video-8744edeac3b4d1ce16b680916b5267ce.zip # mouse E
  • New dataset gin.g-node.org/pollytur/sensorium_2023_dataset with Mouse F, G, H, I, J. 
    dynamic29515-10-12-Video-9b4f6a1a067fe51e15306b9628efea20.zip # mouse F
dynamic29623-4-9-Video-9b4f6a1a067fe51e15306b9628efea20.zip # mouse G
dynamic29647-19-8-Video-9b4f6a1a067fe51e15306b9628efea20.zip # mouse H
dynamic29712-5-9-Video-9b4f6a1a067fe51e15306b9628efea20.zip # mouse I
dynamic29755-2-8-Video-9b4f6a1a067fe51e15306b9628efea20.zip # mouse J

Data information

  • See gin.g-node.org/pollytur/Sensorium2023Data/src/master/README.md
  • Each zip file consists of two folders data/ and meta/.
  • data/: includes the variables that were recorded during the experiment. The experimental variables are saved as a collection of NumPy arrays. Each NumPy array contains the value of that variable at a specific image presentation (i.e. trial). Note that the name of the files does not contain any information about the order or time at which the trials took place in experimental time. They are randomly ordered.
    • videos: This directory contains NumPy arrays where each single X.npy contains the video that was shown to the mouse in trial X.
    • responses: This directory contains NumPy arrays where each single X.npy contains the deconvolved calcium traces (i.e. responses) recorded from the mouse in trial X in response to the particular presented image.
    • behavior: Behavioral variables include pupil dilation and running speed. The directory contain NumPy arrays (of size 1 x 2) where each single X.npy contains the behavioural variables (in the same order that was mentioned earlier) for trial X.
    • pupil_center: the eye position of the mouse, estimated as the centre of the pupil. The directory contain NumPy arrays (of size 1 x 2) for horizontal and vertical eye positions.
  • meta/: includes metadata of the experiment.
    • neurons: This directory contains neuron-specific information. Below is a list of important variables in this directory
      • cell_motor_coordinates.npy: contains the position (x, y, z) of each neuron in the cortex, given in microns.
    • statistics: This directory contains statistics (i.e. mean, median, etc.) of the experimental variables (i.e. behaviour, images, pupil_center, and responses). Note: The statistics of the responses are or particular importance, because we provide the deconvolved calcium traces here in the responses. However, for the evaluation of submissions in the competition, we require the responses to be standardized (i.e. r = r/(std_r)).
    • trials: This directory contains trial-specific meta data.
      • tiers.npy: contains labels that are used to split the data into train, validation, and test set.
        • The training and validation split is only present for convenience, and is used by our ready-to-use PyTorch DataLoaders.
        • The test set is used to evaluate the model performance. In the competition datasets, the responses to all test images is withheld.

Rochefort Lab dataset

  • The rochefort-lab dataset (day 1 recordings) was recorded and structured to be as close to the Sensorium 2023 dataset as possible.