Structured connectivity in the output of the cerebellar cortex
Bibliographic record
Abstract
Abstract Circuits in the brain are built from connections between neurons, where the spatial organization and functional properties of these connections determines circuit function. In the cerebellum, Purkinje cells transmit information to neurons in the cerebellar nuclei, but how Purkinje cell – nuclear neuron connections are organized remains unclear. Here, we explored the connections between Purkinje cells and cerebellar nuclear neurons using whole-cell electrophysiology and optogenetics to produce spatial connectivity maps of cerebellar cortical output. We observed non-random connectivity between Purkinje cells and their target neurons, with inputs to cerebellar nuclear neurons clustering along cerebellar transverse zones. While many nuclear neurons received inputs from a single zone, a number of different connectivity motifs were observed. Neurons receiving inputs from all four zones were more common than predicted by a random model and showed topographic organization in the nucleus. Finally, we observed that small Purkinje cell inputs were sufficient to pause the output of nuclear neurons, suggesting that widespread Purkinje cell synchrony may not be necessary to influence cerebellar output. These findings reveal cerebellar nuclear neurons as an important locus of multimodal cerebellar integration.
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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) | 0.001 | 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".