  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.

Neurons and Cognition