Spatiotemporal Properties of Fast and Slow Neurons in the Pretectal Nucleus Lentiformis Mesencephali in Pigeons
Bibliographic record
Abstract
Neurons in the pretectal nucleus lentiformis mesencephali (LM) are involved in the analysis of optic flow that results from self-motion. Previous studies have shown that LM neurons have large receptive fields in the contralateral eye, are excited in response to largefield stimuli moving in a particular (preferred) direction, and are inhibited in response to motion in the opposite (anti-preferred) direction. We investigated the responses of LM neurons to sine wave gratings of varying spatial and temporal frequency drifting in the preferred and anti-preferred directions. The LM neurons fell into two categories. "Fast" neurons were maximally excited by gratings of low spatial [0.03-0.25 cycles/ degrees (cpd)] and mid-high temporal frequencies (0.5-16 Hz). "Slow" neurons were maximally excited by gratings of high spatial (0.35-2 cpd) and low-mid temporal frequencies (0.125-2 Hz). Of the slow neurons, all but one preferred forward (temporal to nasal) motion. The fast group included neurons that preferred forward, backward, upward, and downward motion. For most cells (81%), the spatial and temporal frequency that elicited maximal excitation to motion in the preferred direction did not coincide with the spatial and temporal frequency that elicited maximal inhibition to gratings moving in the anti-preferred direction. With respect to motion in the anti-preferred direction, a substantial proportion of the LM neurons (32%) showed bi-directional responses. That is, the spatiotemporal plots contained domains of excitation in addition to the region of inhibition. Neurons tuned to stimulus velocity across different spatial frequency were rare (5%), but some neurons (39%) were tuned to temporal frequency. These results are discussed in relation to previous studies of the responses of neurons in the accessory optic system and pretectum to drifting gratings and other largefield 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 | 0.000 | 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".