Fabian Sinz, Andreas Tolias, Alexander Ecker, Konstantin Willeke, Santiago Cadena, Paul Fahey, Mohammad Bashiri, Laura Pede, Max Burg, Christoph Blessing, Zhiwei Ding, Konstantin-Klemens Lurz, and 3 more
We lifted 6 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| sinzlab/sensorium | canonical | 4 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| get_df_for_scores | Ran | sinzlab/sensorium/sensorium/utility/measure_helpers.py code served (permissive licence) · get_code("017b80ebb4fecbee") |
| lerp | Ran | sinzlab/sensorium/sensorium/utility/measure_helpers.py code served (permissive licence) · get_code("0583d3a4035ee91a") |
| prepare_grid | Ran | sinzlab/sensorium/sensorium/models/utility.py code served (permissive licence) · get_code("e3e59ee75115bd54") |
| serp | Ran | sinzlab/sensorium/sensorium/utility/measure_helpers.py code served (permissive licence) · get_code("3fe943ac6bc777af") |
| get_data_hub_loader | Not yet run | sinzlab/sensorium/sensorium/utility/submission.py code served (permissive licence) · get_code("4ad7bca6e6e20cc9") |
| split_images | Not yet run | sinzlab/sensorium/sensorium/utility/scores.py code served (permissive licence) · get_code("c6975dd95d700661") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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, pretrained 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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2206.08666")
get_code_for_paper("2206.08666")
have("2206.08666")
Connect an agent — have() is free.