Spatiotemporal Tuning of Optic Flow Inputs to the Vestibulocerebellum in Pigeons: Differences Between Mossy and Climbing Fiber Pathways
Bibliographic record
Abstract
The pretectum, accessory optic system (AOS), and vestibulocerebellum (VbC) have been implicated in the analysis of optic flow and generation of the optokinetic response. Recently, using drifting sine-wave gratings as stimuli, it has been shown that pretectal and AOS neurons exhibit spatiotemporal tuning. In this respect, there are two groups: fast neurons, which prefer low spatial frequency (SF) and high temporal frequency (TF) gratings, and slow neurons, which prefer high SF-low TF gratings. In pigeons, there are two pathways from the pretectum and AOS to the VbC: a climbing fiber (CF) pathway to Purkinje cells (P cells) via the inferior olive and a direct mossy fiber (MF) pathway to the granular layer (GL). In the present study, we assessed spatiotemporal tuning in the VbC of ketamine-anesthetized pigeons using standard extracellular techniques. Recordings were made from 17 optic-flow-sensitive units in the GL, presumably granule cells or MF rosettes, and the complex spike activity (CSA) of 39 P-cells, which reflects CF input. Based on spatiotemporal tuning to gratings moving in the preferred direction, eight GL units were classified as fast units, with a primary response to low SF-high TF gratings (mean = 0.13 cpd/8.24 Hz), whereas nine were slow units preferring high SF-low TF gratings (mean = 0.68 cpd/0.30 Hz). CSA was almost exclusively tuned to slow gratings (mean = 0.67 cpd/0.35 Hz). We conclude that MF input to the VbC is from both fast and slow cells in the AOS and pretectum, whereas the CF input is primarily tuned to slow gratings.
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) | 0.000 | 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".