Reduction in Noise Correlation is Associated with Improved Behavioural Performance
Bibliographic record
Abstract
Background: Visual perception constitutes the dominant method by which we process our environment, yet the neuronal substrates that underlie visual perception in the brain are not well understood. Noise correla- tion, defined as the correlation in non-stimulus evoked activity between neurons, has been shown to impact both encoding and decoding processes of visual stimuli. We wanted to determine whether changes in noise correlation can predict behavioural performance in a coherent motion-detection task. Methods: Two macaque monkeys (Macaca mulatta) were trained in a coherent motion-detection task, where they learned to fixate on a screen and anticipate the onset of a motion coherence stimulus. During this task, spike activity from pairs of neurons of the middle temporal area (area MT) were recorded and data was analyzed using MATLAB. Specifically, we examined noise correlation as a function of time and success rate in the task. Results: We found a decrease in the correlation in activity between neurons in area MT prior to the onset of the motion coherence stimulus. This decrease was accompanied by improved behavioural performance in the motion coherence-detection task. Limitations: The activity in pairs of neurons may not accurately represent overall activity in a population of neurons. In addition, control experiments to better assess the nature of the common input that leads to a reduction in noise correlation were not conducted. Conclusions: Despite these limitations, we have shown that a reduction in noise correlation prior to stimulus onset is accompanied by improved behavioural performance, suggesting that noise correlation may be a critical parameter that can aid in our understanding of how visual perception occurs in the brain.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".