Custom-Built Operant Conditioning Setup for Calcium Imaging and Cognitive Testing in Freely Moving Mice
Bibliographic record
Abstract
Operant chambers are widely used in animal research to study cognition, motivation, and learning processes. Paired with the rapidly developing technologies for brain imaging and manipulations of brain activity, operant conditioning chambers are a powerful tool for neuroscience research. The behavioral testing and imaging setups that are commercially available are often quite costly. Here, we present a custom-built operant chamber that can be constructed in a few days by an unexperienced user with relatively inexpensive, widely available materials. The advantages of our operant setup compared with other open-source and closed-source solutions are its relatively low cost, its support of complex behavioral tasks, its user-friendly setup, and its validated functionality with video imaging of behavior and calcium imaging using the UCLA Miniscope. Using this setup, we replicate our previously published findings showing that mice exposed to social defeat stress in adolescence have inhibitory control impairments in the Go/No-Go task when they reach adulthood. We also present calcium imaging data of medial prefrontal cortex (mPFC) neuronal activity acquired during Go/No-Go testing in freely moving mice and show that neuronal population activity increases from day 1 to day 14 of the task. We propose that our operant chamber is a cheaper alternative to its commercially available counterparts and offers a better balance between versatility and user-friendly setup than other open-source alternatives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".