Misbehaving Fish: The role of these simple vertebrates in the analysis of human brain function and dysfunction
Bibliographic record
Abstract
Analysis of behaviour was the first approach with which the functioning of the brain could be studied in the distant past. The simple beginnings have led to one of the most sophisticated, fast-paced, burgeoning fields of biology research, neuroscience. Analysis of behaviour remains an integral part of neuroscience, and has led to the formation of the subfield of behavioural neuroscience. Here, I focus on this subfield, and specifically on the role of research with fish in it. I review why behavioural analysis is important for brain research and why studying this cluster of phenotypes with fish is particularly important and useful. I discuss the use of zebrafish for modeling human brain disorders including anxiety, addiction, neurodegenerative diseases and sleep disorders. I also review why extending behavioural neuroscience research to other fish species may benefit brain research. Last, I speculate about the near future, and discuss novel concepts and techniques, new directions behavioural neuroscience research with fish may take. The review is not comprehensive and reflects my own personal biases. Many of the examples are drawn from studies conducted in my own laboratory. Nevertheless, I hope that the examples I discuss will provide a reasonable snapshot of the current state-of-affairs and of the proximate future of behavioural neuroscience studies with fish, and that they will persuade the reader to use these simple vertebrates, and benefit from the reductionist approach they offer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".