A cGMP-Dependent Protein Kinase Gene, <i>foraging</i>, Modifies Habituation-Like Response Decrement of the Giant Fiber Escape Circuit in <i>Drosophila</i>
Bibliographic record
Abstract
The Drosophila giant fiber jump-and-flight escape response is a model for genetic analysis of both the physiology and the plasticity of a sensorimotor behavioral pathway. We previously established the electrically induced giant fiber response in intact tethered flies as a model for habituation, a form of nonassociative learning. Here, we show that the rate of stimulus-dependent response decrement of this neural pathway in a habituation protocol is correlated with PKG (cGMP-Dependent Protein Kinase) activity and foraging behavior. We assayed response decrement for natural and mutant rover and sitter alleles of the foraging (for) gene that encodes a Drosophila PKG. Rover larvae and adults, which have higher PKG activities, travel significantly farther while foraging than sitters with lower PKG activities. Response decrement was most rapid in genotypes previously shown to have low PKG activities and sitter-like foraging behavior. We also found differences in spontaneous recovery (the reversal of response decrement during a rest from stimulation) and a dishabituation-like phenomenon (the reversal of response decrement evoked by a novel stimulus). This electrophysiological study in an intact animal preparation provides one of the first direct demonstrations that PKG can affect plasticity in a simple learning paradigm. It increases our understanding of the complex interplay of factors that can modulate the sensitivity of the giant fiber escape response, and it defines a new adult-stage phenotype of the foraging locus. Finally, these results show that behaviorally relevant neural plasticity in an identified circuit can be influenced by a single-locus genetic polymorphism existing in a natural population of Drosophila.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".