The Omniroute maze: a novel rodent navigation apparatus that integrates dynamically configurable routes, sensory cues and automated reward delivery
Bibliographic record
Abstract
Abstract We developed the Omniroute maze, a novel apparatus that enables near-fully automated, high-throughput behavioral experiments in freely moving rodents, combining the experimental control of virtual reality (VR) platforms with the ethological validity of real-world navigation. The maze features movable wall segments that can be programmatically configured to create unique routes within a 90 × 90 cm platform. Four projectors arrayed around the maze perimeter can display distinct visual cues on both sides of any subset of raised gate wall panels and the maze floor as well as play directional auditory cues. A motorized gantry delivers food-based rewards anywhere in the maze. Real-time 3D tracking of the rat and the reward gantry enables closed-loop control of maze topology, cue configuration, and reward delivery. All subsystems are controlled using the Robot Operating System (ROS) framework via a custom Python-based user interface. The Omniroute can reproduce classic behavioral mazes and a variety of novel configurations to test hypotheses on the interactions between routes, cues, and behavior. Automated configuration, tracking, and reward delivery enable high-throughput experiments on complex navigation behaviors without the potential biases introduced by direct experimenter intervention. Designed from the ground up for robust operation, the Omniroute system utilizes affordable hardware and software to facilitate easy fabrication and assembly as well as replicability by other researchers. Significance Statement The Omniroute system enables high-throughput, automated rodent 2D navigation experiments with real-time control over routes, cues, and reward delivery, comparable to that afforded by modern virtual reality-based systems. In addition, however, the Omniroute supports uninterrupted movement-related behavior, which is critical for assessing the ongoing influence of self-motion cues, while still minimizing confounds from experimenter handling. Naturalistic food-based reinforcement at any location allows for closed-loop behavioral conditioning under diverse task structures. The Omniroute system is one of very few platforms that support near-complete control of available paths, dynamic visual and auditory cues, and naturalistic food-based reinforcement with minimal direct experimenter intervention.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".