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The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos

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 neuroscie...

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Autores principales: Turishcheva, Polina, Fahey, Paul G., Hansel, Laura, Froebe, Rachel, Ponder, Kayla, Vystrčilová, Michaela, Willeke, Konstantin F., Bashiri, Mohammad, Wang, Eric, Ding, Zhiwei, Tolias, Andreas S., Sinz, Fabian H., Ecker, Alexander S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312815/
https://www.ncbi.nlm.nih.gov/pubmed/37396602
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author Turishcheva, Polina
Fahey, Paul G.
Hansel, Laura
Froebe, Rachel
Ponder, Kayla
Vystrčilová, Michaela
Willeke, Konstantin F.
Bashiri, Mohammad
Wang, Eric
Ding, Zhiwei
Tolias, Andreas S.
Sinz, Fabian H.
Ecker, Alexander S.
author_facet Turishcheva, Polina
Fahey, Paul G.
Hansel, Laura
Froebe, Rachel
Ponder, Kayla
Vystrčilová, Michaela
Willeke, Konstantin F.
Bashiri, Mohammad
Wang, Eric
Ding, Zhiwei
Tolias, Andreas S.
Sinz, Fabian H.
Ecker, Alexander S.
author_sort Turishcheva, Polina
collection PubMed
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 (i.e. images). 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 Benchmark Competition with dynamic input (https://www.sensorium-competition.net/). This competition 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 (i.e. video). 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.
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spelling pubmed-103128152023-07-01 The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos Turishcheva, Polina Fahey, Paul G. Hansel, Laura Froebe, Rachel Ponder, Kayla Vystrčilová, Michaela Willeke, Konstantin F. Bashiri, Mohammad Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. ArXiv Article 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 (i.e. images). 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 Benchmark Competition with dynamic input (https://www.sensorium-competition.net/). This competition 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 (i.e. video). 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. Cornell University 2023-05-31 /pmc/articles/PMC10312815/ /pubmed/37396602 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Turishcheva, Polina
Fahey, Paul G.
Hansel, Laura
Froebe, Rachel
Ponder, Kayla
Vystrčilová, Michaela
Willeke, Konstantin F.
Bashiri, Mohammad
Wang, Eric
Ding, Zhiwei
Tolias, Andreas S.
Sinz, Fabian H.
Ecker, Alexander S.
The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
title The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
title_full The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
title_fullStr The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
title_full_unstemmed The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
title_short The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
title_sort dynamic sensorium competition for predicting large-scale mouse visual cortex activity from videos
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312815/
https://www.ncbi.nlm.nih.gov/pubmed/37396602
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