Angular Tuning Bias of Vibrissa-Responsive Cells in the Paralemniscal Pathway
Bibliographic record
Abstract
One of the most salient features of primary vibrissal afferents is their sensitivity to the direction in which the vibrissae move. Directional sensitivity is also well conserved in brainstem, thalamic, and cortical neurons of the lemniscal pathway, indicating that this property plays a key role in the organization of the vibrissal system. Here, we show that directional tuning is also a fundamental feature of second-order interpolaris neurons that give rise to the paralemniscal pathway. Quantitative assessment of responses to vibrissa deflection revealed an anisotropic organization of receptive fields with regard to topography, response magnitude, and the degree of angular tuning. Responses evoked by all vibrissae within the receptive field of each cell exhibited a high consistency of direction preference, but a striking difference in angular tuning preference was found among cells that reside in the rostral and caudal divisions of the interpolaris nucleus. Although in caudal interpolaris vectors of angular preference pointed in all directions, in rostral interpolaris virtually all vectors pointed upward, revealing a strong preference for this direction. Control experiments showed that the upward bias did not rely on a preferential innervation of rostral cells by upwardly tuned primary vibrissa afferents, nor did it rely on a direction-selective recruitment of feedforward inhibition. We thus propose that the upward preference bias of rostral cells, which project to the posterior group of the thalamus, emerges from use-dependent synaptic processes that relate to the kinematics of whisking.
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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) | 0.001 | 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".