Neuronal population activity in area 8a of macaques predicts saccade end point
Bibliographic record
Abstract
Previous electrophysiological studies have demonstrated that single neurons in area 8a of macaques are involved in the planning and execution of saccadic eye movements. However, most of these studies have recorded from one neuron at a time and pooled neuronal responses over multiple non-simultaneous single trial presentations of the same condition. This procedure assumes that responses of single neurons are independent (non-correlated), an assumption that has proven to be erroneous. Here, we implanted 96-channels multielectrode arrays in the area 8a of two macaques and recorded single and multiunit activity while they performed a visually guided saccade task to one out of four simultaneously presented targets. Simultaneous neuronal spiking activity aligned to the initiation of the saccade was inputted into a support vector machine (SVM) algorithm to optimize a model predicting to which quadrant of the screen the monkey would saccade. Including all neurons, the model achieved 67% accuracy in predicting saccade end point. Including only neurons with significant spatial tuning increased the model accuracy to 94%. Interestingly, the analysis period that yielded the best accuracy spanned from saccade initiation to 80 msec before, longer or shorter time intervals slightly diminishing model performance. In order to assess the effect of the simultaneity of the neuronal activity on model performance, we destroyed simultaneity by shuffling trials’ identity within experimental conditions, which concomitantly abolishes noise correlations. Surprisingly, noise-correlations-free activity yielded significantly better prediction accuracy both when all neurons were included (+9%) and when only spatially tuned neurons were selected (+4%). These preliminary results suggest that neuronal population activity in the macaque area 8a can be decoded to predict stereotyped saccade end point with high accuracy, and that noise correlations have a slight detrimental effect on population decoding using a SVM classifier. Meeting abstract presented at VSS 2013
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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".