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Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity
Polina Turishcheva1, Paul G Fahey2,3,4,5, Michaela Vystrčilová1
1Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany.
Arxiv
|July 23, 2024
Summary
A new benchmark competition, SENSORIUM 2023, was created to evaluate dynamic models of the mouse visual system. Winning models significantly improved predictive accuracy for neuronal responses to visual stimuli.
Area of Science:
- Computational Neuroscience
- Machine Vision
- Artificial Neural Networks
Background:
- Biological visual system processing is complex due to nonlinear input-response relationships.
- Artificial neural networks offer tools for modeling biological and machine vision.
- Lack of standardized benchmarks hinders progress in dynamic models of the mouse visual system.
Purpose of the Study:
- To establish a standardized benchmark for evaluating dynamic models of the mouse visual system.
- To create a large-scale dataset of neuronal responses and behavior from the mouse primary visual cortex.
- To foster advancements in predictive modeling for neuroscience.
Main Methods:
- Established the SENSORIUM 2023 Benchmark Competition with dynamic input.
- Collected a large dataset from 78,853 mouse neurons over 2 hours, including behavioral data.
- Ranked over 160 model submissions from 22 teams on in-domain and out-of-distribution prediction tasks.
Main Results:
- The competition successfully ranked dynamic models based on predictive performance.
- New neural network architectures were proposed, improving state-of-the-art by 50%.
- Models were evaluated on their ability to generalize to novel stimuli.
Conclusions:
- The SENSORIUM benchmark provides a vital resource for advancing computational models of the visual system.
- The competition spurred innovation in neural network architectures for neuroscience.
- The dataset and infrastructure are available for continued research.
