A modular gate system for autonomous control of rodent behavior
Bibliographic record
Abstract
Abstract Rodent mazes have been used for decades to study the neural basis of behavior. Advancements in rapid prototyping techniques and access to affordable electronics allows laboratories with sufficient expertise in engineering and programming to customize and construct maze apparatuses and behavioral tasks, thereby increasing the ability of their studies to answer specific scientific questions. We designed and built a rodent gate system that lowers this bar of expertise even further. The NC4gate system is a robust mechanical design that can be built using low-cost hardware and execute thousands of cycles before maintenance. Up to 512 gates can be controlled using a single computer. Users can control the gates interactively using a Python-based graphical interface and programmatically using an extensible API. We hope that the open-source hardware / software and extensive documentation enables laboratories to build these affordable and robust gates and seamlessly incorporate automatic behavior control into their existing or new rodent tasks. Significance Statement Rodent mazes are used by thousands of laboratories and research institutions across the world to study learning and memory, as well as the effects of pharmacological, genetic and environmental manipulations. Ideally, maze and task designs should be customized to the scientific questions at hand. It is challenging, however, for many laboratories to build, program, and operate custom mazes, requiring them instead to rely on expensive and proprietary commercial solutions. The most complex components of most mazes are moving gates that restrict and direct rodent behavior. Here we provide the open-source hardware and software for a gate system that is extensible, affordable and robust, removing this critical barrier to customized mazes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".