Inhibitory medial zona incerta pathway drives exploratory behavior by inhibiting glutamatergic cuneiform neurons
Bibliographic record
Abstract
The cuneiform nucleus (CnF) regulates locomotor activity, which is canonically viewed as being primarily involved in initiating locomotion and regulating speed. Recent research shows greater context dependency in the locomotor functions of this nucleus. Glutamatergic neurons, which contain vesicular glutamate transporter 2 (vGLUT2), regulate context-dependent locomotor speed in the CnF and play a role in defensive behavior. Here, we identify projections from the medial zona incerta (mZI) to CnF vGLUT2 neurons that promote exploratory behavior. Using fiber photometry recordings in male mice, we find that mZI gamma-aminobutyric acid (GABA) neurons increase activity during periods of exploration. Activation of mZI GABAergic neurons is associated with reduced spiking of CnF neurons. Additionally, activating both retrogradely labeled mZI-CnF GABAergic projection neurons and their terminals in the CnF increase exploratory behavior. Inhibiting CnF vGLUT2 neuronal activity also increases exploratory behavior. These findings provide evidence for the context-dependent dynamic regulation of CnF vGLUT2 neurons, with the mZI-CnF circuit shaping exploratory behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".