Effects of neural ensemble size and composition on the decoding of attention in primate lateral prefrontal cortex
Bibliographic record
Abstract
The allocation of attention can be decoded from the activity of lateral prefrontal cortex neuronal ensembles (Tremblay et al., 2015). One issue that remains unclear is the impact of a neural population's size and composition on decoding of attention. To investigate this, we recorded the responses of neurons in lateral prefrontal cortex of two macaques using microelectrode arrays while they performed a visuospatial attention task. During the task, the animals had to direct attention to a cued target stimulus positioned in one of the four visual quadrants while ignoring 3 identical distractors positioned in the remaining quadrants. We systematically changed the size and composition of the neuronal ensembles, as well as the pattern of noise correlations, and evaluated their information content using a linear decoder. First, we found that the location of visuospatial attention was reliably decoded from ensembles of approximately 50 units (mean accuracy = 76%, p< 0.05, Permutation test). We progressively increased the number of neurons in an ensemble from 1 to 50 units and assessed decoding performance using two methods; first, we built subnetworks of most informative neurons, and second, we built subnetworks that maximized information of the ensemble. We found that the decoding performance of the most informative subnetworks was higher than those composed of the most informative units. Interestingly, the most informative subnetworks were not necessarily comprised of most informative units, including in many cases non-selective units (Kruskal-Wallis test P>0.05). Finally, removing noise correlations increased the decoding performance of ensembles of most informative units (6%, Signed rank P< 0.01), whereas removing correlations in most informative subnetworks of equivalent size had no effect on performance (Signed rank P>0.05). These results indicate a complex effect of ensemble size and composition on the coding of attention in lateral prefrontal cortex neuronal ensembles. Meeting abstract presented at VSS 2016
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".