Multisensory integration in early vestibular processing in mice: the encoding of passive vs. active motion
Bibliographic record
Abstract
The mouse has become an important model system for studying the cellular basis of learning and coding of heading by the vestibular system. Here we recorded from single neurons in the vestibular nuclei to understand how vestibular pathways encode self-motion under natural conditions, during which proprioceptive and motor-related signals as well as vestibular inputs provide feedback about an animal's movement through the world. We recorded neuronal responses in alert behaving mice focusing on a group of neurons, termed vestibular-only cells, that are known to control posture and project to higher-order centers. We found that the majority (70%, n = 21/30) of neurons were bimodal, in that they responded robustly to passive stimulation of proprioceptors as well as passive stimulation of the vestibular system. Additionally, the linear summation of a given neuron's vestibular and neck sensitivities predicted well its responses when both stimuli were applied simultaneously. In contrast, neuronal responses were suppressed when the same motion was actively generated, with the one striking exception that the activity of bimodal neurons similarly and robustly encoded head on body position in all conditions. Our results show that proprioceptive and motor-related signals are combined with vestibular information at the first central stage of vestibular processing in mice. We suggest that these results have important implications for understanding the multisensory integration underlying accurate postural control and the neural representation of directional heading in the head direction cell network of mice.
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".