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File structure

Please download and store the dataset in the following structure. Our code automatically unzips the zip files if folder of the same name does not exist. Also note that, as part of the data preprocessing step, we recompute the statistics of the data from the training set and stored them under <mouse_dir>/statistics.pkl.

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├── README.md
└── sensorium
    ├── dynamic29156-11-10-Video-8744edeac3b4d1ce16b680916b5267ce
    │   ├── config.json
    │   ├── data
    │   │   ├── behavior
    │   │   ├── pupil_center
    │   │   ├── responses
    │   │   └── videos
    │   ├── meta
    │   │   ├── neurons
    │   │   ├── statistics
    │   │   └── trials
    │   └── statistics.pkl
    ├── dynamic29228-2-10-Video-8744edeac3b4d1ce16b680916b5267ce
    ├── dynamic29234-6-9-Video-8744edeac3b4d1ce16b680916b5267ce
    ├── dynamic29513-3-5-Video-8744edeac3b4d1ce16b680916b5267ce
    ├── dynamic29514-2-9-Video-8744edeac3b4d1ce16b680916b5267ce
    ├── dynamic29515-10-12-Video-9b4f6a1a067fe51e15306b9628efea20
    ├── dynamic29623-4-9-Video-9b4f6a1a067fe51e15306b9628efea20
    ├── dynamic29647-19-8-Video-9b4f6a1a067fe51e15306b9628efea20
    ├── dynamic29712-5-9-Video-9b4f6a1a067fe51e15306b9628efea20
    └── dynamic29755-2-8-Video-9b4f6a1a067fe51e15306b9628efea20

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 contains NumPy arrays (of size 1 x 2) where each single X.npy contains the behavioral variables (in the same order that was mentioned earlier) for trial X.
    • pupil_center: the eye position of the mouse, estimated as the center of the pupil. The directory contains 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 are 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. Note: The
    • statistics: This directory contains statistics (i.e. mean, median, etc.) of the experimental variables (i.e. behavior, images, pupil_center, and responses). Note: The statistics of the responses are of 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.