Complex motion integration in the cat's LP-pulvinar
Bibliographic record
Abstract
We have previously shown that neurons in the cat's lateral posterior-pulvinar (LP-pulvinar) complex are involved in higher-order visual processing (Merabet et al., Nature, 396, 1998). To further investigate this issue, we examined whether neurons in the LP-pulvinar complex can integrate local motion cues into a coherent percept. Experiments were performed on anesthetized normal adult cats. The cells' sensitivity to complex motion was studied using moving random dot kinematograms (RDKs) consisting of a two frame random dot motion sequence, in which each dot survived for a brief period of time (16–160 ms) before being replaced by its partner dot (the dots moved only once before being randomly repositioned). The influence of spatial and temporal intervals between partner dots, stimulus area, relation between global and pattern-motion selectivity were studied. We recorded from 76 direction-selective cells. Out of these, 38% cells responded to the coherent direction of the complex RDKs pattern. Responses varied as a function of spatial and temporal characteristics of the stimulus: cell discharges were generally optimal for short temporal intervals (16 ms) and for large displacements (>2 deg). These observations indicate that the processing of global motion by LP-pulvinar neurons involves higher-order spatiotemporal integration. We have also found that neurons sensitive to complex RDKs were not pattern-selective when tested with drifting plaids, suggesting that the nature of integration underlying responses to the two kind of patterns differs. These findings demonstrates that thalamic cells can integrate local signals into a global percept, further indicating that the extrageniculate thalamus may actively participate in higher-order visual processing. Furthermore, these results provide evidence that there may be specialized mechanisms for different types of complex motion within the LP-pulvinar complex.
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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 | 0.001 |
| 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".