Background complexity affects response of a looming-sensitive neuron to object motion
Bibliographic record
Abstract
An increasing number of studies show how stimulus complexity affects the responses of looming-sensitive neurons across multiple animal taxa. Locusts contain a well-described, descending motion-sensitive pathway that is preferentially looming sensitive. However, the lobula giant movement detector/descending contralateral movement detector (LGMD/DCMD) pathway responds to more than simple objects approaching at constant, predictable trajectories. In this study, we presented Locusta migratoria with a series of complex three-dimensional visual stimuli presented while simultaneously recording DCMD activity extracellularly. In addition to a frontal looming stimulus, we used a combination of compound trajectories (nonlooming transitioning to looming) presented at different velocities and onto a simple, scattered, or progressive flow field background. Regardless of stimulus background, DCMD responses to looming were characteristic and related to previously described effects of azimuthal approach angle and velocity of object expansion. However, increasing background complexity caused reduced firing rates, delayed peaks, shorter rise phases, and longer fall phases. DCMD responded to transitions to looming with a characteristic drop in a firing rate that was relatively invariant across most stimulus combinations and occurred regardless of stimulus background. Spike numbers were higher in the presence of the scattered background and reduced in the flow field background. We show that DCMD response time to a transition depends on unique expansion parameters of the moving stimulus irrespective of background complexity. Our results show how background complexity shapes DCMD responses to looming stimuli, which is explained within a behavioral context.
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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 | 0.000 | 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".