Advancements in neural closed-loop manipulations in awake, behaving animals
Bibliographic record
Abstract
In recent years, there has been a paradigm shift in experimental neuroscience, using emerging technologies to ‘close the loop’ around the nervous system. These experiments measure or stimulate neural activity in the brain of awake, behaving animals based on behavioral or neural variables analyzed in real time. Advancements in position tracking and miniaturized sensors enable neural stimulation to be applied based on complex behavioral or physiological variables. Machine learning can predict and validate optimal behavioral stimuli that elicit a desired neural response, and animals can even be trained to elicit specific neural patterns for reward. Advancements in simultaneous neural recording and stimulation through electrical, optical, acoustic, and chemical channels allow neural activity patterns to dictate neural stimulation. This modifies the nature of neural computation in ways that allow us to dissect and model its components. We survey and present these neural closed-loop manipulations based on their feedback modes and discuss the resultant scientific advancements and remaining challenges.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".