Spatiotemporal profiles of receptive fields of neurons in the lateral posterior nucleus of the cat LP-pulvinar complex
Bibliographic record
Abstract
The pulvinar is the largest extrageniculate thalamic visual nucleus in mammals. It establishes reciprocal connections with virtually all visual cortexes and likely plays a role in transthalamic cortico-cortical communication. In cats, the lateral posterior nucleus (LP) of the LP-pulvinar complex can be subdivided in two subregions, the lateral (LPl) and medial (LPm) parts, which receive a predominant input from the striate cortex and the superior colliculus, respectively. Here, we revisit the receptive field structure of LPl and LPm cells in anesthetized cats by determining their first-order spatiotemporal profiles through reverse correlation analysis following sparse noise stimulation. Our data reveal the existence of previously unidentified receptive field profiles in the LP nucleus both in space and time domains. While some cells responded to only one stimulus polarity, the majority of neurons had receptive fields comprised of bright and dark responsive subfields. For these neurons, dark subfields' size was larger than that of bright subfields. A variety of receptive field spatial organization types were identified, ranging from totally overlapped to segregated bright and dark subfields. In the time domain, a large spectrum of activity overlap was found, from cells with temporally coinciding subfield activity to neurons with distinct, time-dissociated subfield peak activity windows. We also found LP neurons with space-time inseparable receptive fields and neurons with multiple activity periods. Finally, a substantial degree of homology was found between LPl and LPm first-order receptive field spatiotemporal profiles, suggesting a high integration of cortical and subcortical inputs 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 | 0.000 |
| 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".