sensorium_2023_dataset/datacite.yml

# Metadata for DOI registration according to DataCite Metadata Schema 4.1.
# For detailed schema description see https://doi.org/10.5438/0014

## Required fields

# The main researchers involved. Include digital identifier (e.g., ORCID)
# if possible, including the prefix to indicate its type.
authors:
  -
    firstname: "Paul"
    lastname: "Fahey"
    affiliation: "Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA"
    id: "ORCID:0000-0001-6844-3551"
  -
    firstname: "Polina"
    lastname: "Turishcheva"
    affiliation: "Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany"
  -
    firstname: "Laura"
    lastname: "Hansel"
    affiliation: "Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany"
  -
    firstname: "Rachel"
    lastname: "Froebe"
    affiliation: "Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA"
  -
    firstname: "Kayla"
    lastname: "Ponder"
    affiliation: "Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA"
  -
    firstname: "Michaela"
    lastname: "Vystrcilová"
    affiliation: "Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany"
  -
    firstname: "Yongrong"
    lastname: "Qiu"
    affiliation: "International Max Planck Research School for Intelligent Systems, University of Tübingen, Germany"
  -
    firstname: "Konstantin"
    lastname: "Willeke"
    affiliation: "International Max Planck Research School for Intelligent Systems, University of Tübingen, Germany"
  -
    firstname: "Mohammad"
    lastname: "Bashiri"
    affiliation: "International Max Planck Research School for Intelligent Systems, University of Tübingen, Germany"
  -
    firstname: "Andreas"
    lastname: "Tolias"
    affiliation: "Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA"
  -
    firstname: "Fabian"
    lastname: "Sinz"
    affiliation: "Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany"
  -
    firstname: "Alexander"
    lastname: "Ecker"
    affiliation: "Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany"

# A title to describe the published resource.
title: "The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos - Dataset"

# Additional information about the resource, e.g., a brief abstract.
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.

# Lit of keywords the resource should be associated with.
# Give as many keywords as possible, to make the resource findable.
keywords:
  - Neuroscience
  - Predictive models
  - Mouse visual cortex
  - System identification

# License information for this resource. Please provide the license name and/or a link to the license.
# Please add also a corresponding LICENSE file to the repository.
license:
  name: "Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License."
  url: "https://creativecommons.org/licenses/by-nc-nd/4.0/"



## Optional Fields

# Funding information for this resource.
# Separate funder name and grant number by comma.
funding:
  - "EXC 2064/1"
  - "SFB 1233"
  - "SFB 1528"
  - "NSF 1707400"
  


# Related publications. reftype might be: IsSupplementTo, IsDescribedBy, IsReferencedBy.
# Please provide digital identifier (e.g., DOI) if possible.
# Add a prefix to the ID, separated by a colon, to indicate the source.
# Supported sources are: DOI, arXiv, PMID
# In the citation field, please provide the full reference, including title, authors, journal etc.
references:
  - 
    id: "doi:10.48550"
    reftype: "IsDescribedBy"
 #   citation: "Citation1"

 # -
 #   id: "arxiv:mmmm.nnnn"
 #   reftype: "IsSupplementTo"
 #   citation: "Citation2"
 # -
 #   id: "pmid:nnnnnnnn"
 #   reftype: "IsReferencedBy"
 #  citation: "Citation3"


# Resource type. Default is Dataset, other possible values are Software, DataPaper, Image, Text.
resourcetype: Dataset

# Do not edit or remove the following line
templateversion: 1.2