Multiunit frontal eye field activity codes the visuomotor transformation, but not gaze prediction or target memory, in a delayed saccade task
Bibliographic record
Abstract
ABSTRACT Single-unit (SU) activity − action potentials isolated from one neuron — has traditionally been employed to relate neuronal activity to behavior. However, recent investigations have shown that multi-unit (MU) activity − ensemble neural activity recorded within the vicinity of one microelectrode − may also contain accurate estimations of task-related neural population dynamics. Here, using a well-established model-fitting approach, we compared the spatial codes of SU response fields with corresponding MU response fields recorded from the frontal eye fields (FEF) in head-unrestrained monkeys ( Macaca mulatta ) during a memory-guided saccade task. We focused on characterizing the visuomotor transformation from Target-in-Eye coordinates to future Gaze-in-Eye coordinates (Sajad et al., 2015). Most SU visual response fields coded targets (with some predicting Gaze), whereas the MU population only coded targets. Most SU motor responses coded Gaze, but many still retained a target code. In contrast, MU motor activity predominantly coded Gaze with very little target coding. Finally, both SU and MU populations showed a progressive transition through intermediate ‘Target-to-Gaze’ codes during the delay period, but the MU activity showed a ‘smoother’ transition. These results confirm the theoretical and practical potential of MU activity recordings as a biomarker for fundamental sensorimotor transformations (e.g., Target-to-Gaze coding in the oculomotor system), while also highlighting the importance of SU activity for coding more cognitive (e.g., predictive / memory) aspects of sensorimotor behavior. SIGNIFICANCE STATEMENT Multi-unit recordings (undifferentiated signals from several neurons) are relatively easy to record and provide a simplified estimate of neural dynamics, but it is not clear which single-unit signals are retained, amplified, or lost. Here, we compared single-/multi-unit activity from a well-defined structure (the frontal eye fields) and behavior (memory-delay saccade task), tracking their spatial codes through time. The progressive transformation from target to gaze coding observed in single-unit activity was retained in multi-unit activity, but gaze prediction (in the visual response) and target memory (in the motor response) were lost. This suggests that multi-unit activity provides an excellent biomarker for healthy sensorimotor transformations, at the cost of missing more subtle cognitive signals.
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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".