Directional information from neuronal ensembles in the primate orofacial sensorimotor cortex
Bibliographic record
Abstract
Neurons in the arm and orofacial regions of the sensorimotor cortex in behaving monkeys display directional tuning of their activity during arm reaching and tongue protrusion, respectively. While studies on population activity abound for the arm motor cortex, how populations of neurons from the orofacial sensorimotor cortex represent direction has never been described. We therefore examined and compared the directional information contained in the spiking activity of populations of single neurons recorded simultaneously from chronically implanted microelectrode arrays in the orofacial primary motor (MIo, N = 345) and somatosensory (SIo, N = 336) cortices of monkeys (Macaca mulatta) as they protruded their tongue in different directions. Differential modulation to the direction of tongue protrusion was found in >60% of task-modulated neurons in MIo and SIo and was stronger in SIo (P < 0.05). Moreover, mutual information between direction and spiking was significantly higher in SIo compared with MIo at force onset and force offset (P < 0.01). Finally, the direction of tongue protrusion was accurately predicted on a trial-by-trial basis from the spiking activity of populations of MIo or SIo neurons by using a discrete decoder (P < 0.01). The highly reliable decoding was comparable between MIo and SIo neurons. However, the temporal evolution of the decoding performance differed between these two areas: MIo showed late-onset, fast-rising, and phasic performance, whereas SIo exhibited early-onset, slow-rising, and sustained performance. Overall, the results suggest that both MIo and SIo are highly involved in representing the direction of tongue protrusion but they differ in the amplitude and temporal processing of the directional information distributed across populations of neurons.
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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) | 0.000 | 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".