An Integrated Platform for in vivo Electrophysiology in Spatial Cognition Experiments
Bibliographic record
Abstract
Abstract Spatial cognition research requires behavioral paradigms that can distinguish between different navigational elements, such as allocentric (map-like) navigation and egocentric (e.g., body centered) navigation. To fill this need, we developed a flexible experimental platform that can be quickly modified without the need for significant changes to software and hardware. In this paper, we present this inexpensive and flexible behavioral platform paired with software which we are making freely available. Our behavioral platform serves as the foundation for a range of experiments, and though developed for assessing spatial cognition, it also has applications in the non-spatial domain of behavioral testing. There are two components of the software platform, ‘Maze’ and ‘Stim Trigger’. Both programs can work in conjunction with electrophysiology acquisition systems, allowing for precise time stamping of neural events with behavior. The Maze program includes functionality for automatic reward delivery based on user defined zones. ‘Stim Trigger’ permits control of brain stimulation via any equipment that can be paired with an Arduino board. We seek to share our software and leverage the potential by expanding functionality in the future to meet the needs of a larger community of researchers. Significance Statement This paper presents an innovative and cost-effective behavioral platform designed to distinguish between different navigational elements, addressing the crucial need for better spatial cognition research paradigms. The platform’s flexibility allows for quick modifications without major software or hardware changes. Additionally, the freely available software, comprising ‘Maze’ and ‘Stim Trigger’ components, enables precise time stamping of neural events with behavior, while facilitating automatic reward delivery and brain stimulation control. Beyond spatial cognition assessment, the platform’s adaptability extends to non-spatial behavioral testing. By openly sharing this software, the authors aim to foster collaboration and encourage future developments, promoting its application to a broader community of researchers. This platform represents a significant advancement in spatial cognition research and behavioral experimentation methods.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".