| Authors |
Fahey,Paul;Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA;ORCID:0000-0001-6844-3551
Turishcheva,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
Qiu,Yongrong;International Max Planck Research School for Intelligent Systems, University of Tübingen, 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
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| 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.
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