Optical imaging in galagos reveals parietal–frontal circuits underlying motor behavior
Bibliographic record
Abstract
The posterior parietal cortex (PPC) of monkeys and prosimian galagos contains a number of subregions where complex, behaviorally meaningful movements, such as reaching, grasping, and body defense, can be evoked by electrical stimulation with long trains of electrical pulses through microelectrodes. Shorter trains of pulses evoke no or simple movements. One possibility for the difference in effectiveness of intracortical microstimulation is that long trains activate much larger regions of the brain. Here, we show that long-train stimulation of PPC does not activate widespread regions of frontal motor and premotor cortex but instead, produces focal, somatotopically appropriate activations of frontal motor and premotor cortex. Shorter stimulation trains activate the same frontal foci but less strongly, showing that longer stimulus trains do not produce less specification. Because the activated sites in frontal cortex correspond to the locations of direct parietal-frontal anatomical connections from the stimulated PPC subregions, the results show the usefulness of optical imaging in conjunction with electrical stimulation in showing functional pathways between nodes in behavior-specific cortical networks. Thus, long-train stimulation is effective in evoking ethologically relevant sequences of movements by activating nodes in a cortical network for a behaviorally relevant period rather than spreading activation in a nonspecific manner.
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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".