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

arXiv: https://arxiv.org/abs/2305.19654 · HTML: https://mlsanity.com/paper/2305.19654/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2023.

## Built on by (most cited first, 11 of 11)

- [NeuralBench: A Unifying Framework to Benchmark NeuroAI Models](https://mlsanity.com/paper/2605.08495/) (2026)
- [Brain Decodes Deep Nets](https://mlsanity.com/paper/2312.01280/) (2023)
- [ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish](https://mlsanity.com/paper/2503.02618/) (2025)
- [Transformer brain encoders explain human high-level visual responses](https://mlsanity.com/paper/2505.17329/) (2025)
- [ResKoopNet: Learning Koopman Representations for Complex Dynamics with Spectral Residuals](https://mlsanity.com/paper/2501.00701/) (2025)
- [Most discriminative stimuli for functional cell type clustering](https://mlsanity.com/paper/2401.05342/) (2023)
- [Modeling Dynamic Neural Activity by combining Naturalistic Video Stimuli and Stimulus-independent Latent Factors](https://mlsanity.com/paper/2410.16136/) (2024)
- [QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex](https://mlsanity.com/paper/2412.07264/) (2024)
- [Aligning Neuronal Coding of Dynamic Visual Scenes with Foundation Vision Models](https://mlsanity.com/paper/2407.10737/) (2024)
- [Higher-Order Convolution Improves Neural Predictivity in the Retina](https://mlsanity.com/paper/2505.07620/) (2025)
- [Discovering Mechanistic Models of Neural Activity: System Identification in an in Silico Zebrafish](https://mlsanity.com/paper/2602.04492/) (2026)

## Abstract (arXiv)

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, 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 Benchmark Competition with dynamic input. It includes the collection of a new large-scale dataset from the primary visual cortex of ten mice, containing responses from over 78,000 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.