The neural underpinning of the biological visual system is challenging to
study experimentally, in particular as the neuronal activity becomes
increasingly nonlinear with respect to visual input. Artificial neural networks
(ANNs) can serve a variety of goals for improving our understanding of this
complex system, not only serving as predictive digital twins of sensory cortex
for novel hypothesis generation in silico, but also incorporating bio-inspired
architectural motifs to progressively bridge the gap between biological and
machine vision. The mouse has recently emerged as a popular model system to
study visual information processing, but no standardized large-scale benchmark
to identify state-of-the-art models of the mouse visual system has been
established. To fill this gap, we propose the Sensorium benchmark competition.
We collected a large-scale dataset from mouse primary visual cortex containing
the responses of more than 28,000 neurons across seven mice stimulated with
thousands of natural images, together with simultaneous behavioral measurements
that include running speed, pupil dilation, and eye movements. The benchmark
challenge will rank models based on predictive performance for neuronal
responses on a held-out test set, and includes two tracks for model input
limited to either stimulus only (Sensorium) or stimulus plus behavior
(Sensorium+). We provide a starting kit to lower the barrier for entry,
including tutorials, pre-trained baseline models, and APIs with one line
commands for data loading and submission. We would like to see this as a
starting point for regular challenges and data releases, and as a standard tool
for measuring progress in large-scale neural system identification models of
the mouse visual system and beyond.

提出一种 Mouse neural system identification 的 benchmark competition，收集了一组大规模的数据集，基于预测性能对不同模型进行排名和评估。