Fast spiking interneuron activity in primate striatum tracks learning of attention cues
Bibliographic record
Abstract
Cognitive flexibility depends on a fast neural learning mechanism for enhancing momentary relevant over irrelevant information. A possible neural mechanism realizing this enhancement uses fast spiking interneurons (FSIs) in the striatum to train striatal projection neurons to gate relevant and suppress distracting cortical inputs. We found support for such a mechanism in nonhuman primates during the flexible adjustment of visual attention in a reversal learning task. FSI activity was modulated by visual attention cues during feature-based learning. One FSI subpopulation showed stronger activation during learning, while another FSI subpopulation showed response suppression after learning, which could indicate a disinhibitory effect on the local circuit. Additionally, FSIs that showed response suppression to learned attention cues were activated by salient distractor events, suggesting they contribute to suppressing bottom-up distraction. These findings suggest that striatal fast spiking interneurons play an important role when cues are learned that redirect attention away from previously relevant to newly relevant visual information. This cue-specific activity was independent of motor-related activity and thus tracked specifically the learning of reward predictive visual features.
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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) | 0.000 | 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".