Polina Turishcheva
981072bc1b
Update 'README.md'
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!!! Note: GIN does not support bulk downloads. You need to download the files individually !!!
This dataset does not have out-of-distribution responses. To download the one with OOD responses additionally - go here - Link
Below we provide a brief explanation of the dataset structure and how to access all the information contained in them.
Have a look at our white paper for in depth description of the data. White paper on arXiv
We provide the datasets in the .zip format. Unzipping them will create two folders data and meta.
X.npy contains the video that was shown to the mouse in trial X.X.npy contains the deconvolved calcium traces (i.e. responses) recorded from the mouse in trial X in response to the particular presented image.1 x 2) where each single X.npy contains the behavioral variables (in the same order that was mentioned earlier) for trial X.1 x 2) for horizontal and vertical eye positions.meta: includes meta data of the experiment
cell_motor_coordinates.npy: contains the position (x, y, z) of each neuron in the cortex, given in microns. Note: Thestatistics: This directory contains statistics (i.e. mean, median, etc.) of the experimental variables (i.e. behavior, images, pupil_center, and 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
This data is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. This license requires that you contact us before you use the data in your own research. In particular, this means that you have to ask for permission if you intend to publish a new analysis performed with this data (no derivative works-clause).
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| Title | The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos - Dataset |
| Authors |
Fahey,Paul;Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA;ORCID:0000-0001-6844-3551
Turushcheva,Polina;Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany Hansel,Laura;Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany Froebe,Rachel;Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA Ponder,Kayla;Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA Vystrcilová,Michaela;Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany Willeke,Konstantin;International Max Planck Research School for Intelligent Systems, University of Tübingen, Germany Bashiri,Mohammad;International Max Planck Research School for Intelligent Systems, University of Tübingen, Germany Tolias,Andreas;Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA Sinz,Fabian;Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany Ecker,Alexander;Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany |
| Description | Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input. However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Competition with dynamic input. This includes the collection of a new large-scale dataset from the primary visual cortex of five mice, containing responses from over 38,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input. We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond. |
| License | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
| References |
[doi:10.48550] (IsDescribedBy)
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| Funding |
EXC 2064/1
SFB 1233 SFB 1528 NSF 1707400 |
| Keywords |
Neuroscience
Predictive models Mouse visual cortex System identification |
| Resource Type |
Dataset |