  Understanding how biological visual systems process information is
challenging because of the nonlinear relationship between visual input and
neuronal responses. Artificial neural networks allow computational
neuroscientists to create predictive models that connect biological and machine
vision. Machine learning has benefited tremendously from benchmarks that
compare different model on the same task under standardized conditions.
However, there was no standardized benchmark to identify state-of-the-art
dynamic models of the mouse visual system. To address this gap, we established
the Sensorium 2023 Benchmark Competition with dynamic input, featuring a new
large-scale dataset from the primary visual cortex of ten mice. This dataset
includes responses from 78,853 neurons to 2 hours of dynamic stimuli per
neuron, together with the behavioral measurements such as running speed, pupil
dilation, and eye movements. The competition ranked models in two tracks based
on predictive performance for neuronal responses on a held-out test set: one
focusing on predicting in-domain natural stimuli and another on
out-of-distribution (OOD) stimuli to assess model generalization. As part of
the NeurIPS 2023 competition track, we received more than 160 model submissions
from 22 teams. Several new architectures for predictive models were proposed,
and the winning teams improved the previous state-of-the-art model by 50%.
Access to the dataset as well as the benchmarking infrastructure will remain
online at www.sensorium-competition.net.
