Responses of a Looming-Sensitive Neuron to Compound and Paired Object Approaches
Bibliographic record
Abstract
The lobula giant movement detector (LGMD) and its target neuron, the descending contralateral movement detector (DCMD), constitute a motion-sensitive pathway in the locust visual system that responds preferentially to objects approaching on a collision course. LGMD receptive field properties, anisotropic distribution of local retinotopic inputs across the visual field, and localized habituation to repeated stimuli suggest that this pathway should be sensitive to approaches of individual objects within a complex visual scene. We presented locusts with compound looming objects while recording from the DCMD to test the effects of nonuniform edge expansion on looming responses. We also presented paired objects approaching from different regions of the visual field at nonoverlapping, closely timed and simultaneous approach intervals to study DCMD responses to multiple looming stimuli. We found that looming compound objects evoked characteristic responses in the DCMD and that the time of peak firing was consistent with predicted values based on a weighted ratio of the half size of each distinct object edge and the absolute approach velocity. We also found that the azimuthal position and interval of paired approaches affected DCMD firing properties and that DCMDs responded to individual objects approaching within 106 ms of each other. Moreover, comparisons between individual and paired approaches revealed that overlapping approaches are processed in a strongly sublinear manner. These findings are consistent with biophysical mechanisms that produce nonlinear integration of excitatory and feed-forward inhibitory inputs onto the LGMD that have been shown to underlie responses to looming stimuli.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| 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".