Complex motion trajectories are represented by a population code from the ensemble activity of multiple motion-sensitive descending interneurons in locusts
Bibliographic record
Abstract
Abstract Adaptive locust flight relies on rapid detection and processing of objects moving in the visual field. One identified neural pathway, comprised of the lobula giant movement detector (LGMD) and the descending contralateral movement detector (DCMD), responds preferentially to approaching objects. The LGMD receives retinotopic inputs from ipsilateral ommatidia and generates spikes in a 1:1 ratio in the DCMD, which synapses contralaterally with multiple locomotion-related neurons. Other motion-sensitive neurons have also been identified in locusts but are not as well characterized. To better understand how locusts process visual information, we used multichannel neural recordings within a stimulus arena and presented various complex visual stimuli to investigate the neural encoding of complex object motion. We found multiple discriminated units that responded uniquely to visual motion and categorized the responses. More units responded to motion trajectories with a looming component, compared to translations across the visual field. Dynamic factor analysis (DFA) of discriminated units revealed common trends that reflect the activity of neural ensembles. The numbers and types of common trends varied among motion trajectories. These results increase the understanding of complex visual motion processing in this tractable system.
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 | 0.000 |
| 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".