Neuronal spiking is better than bursting at predicting motion detection in area MT
Bibliographic record
Abstract
Background: The middle temporal visual area (MT) is widely studied in visual processing and in integration of motion signals to form general perceptions. The objective of this study is to determine whether neuronal bursting in area MT of monkeys is more predictive of motion detection than neuronal spiking. Methods: Two Macaca mulatta (macaque) monkeys were trained in Dr. Erik Cook’s lab to detect coherent motion while connected to microelectrodes that determined their neuronal spiking activities. Using MatLab, we manipulated the collected data to determine whether spiking or bursting is more predictive of motion detection. Results: We repeatedly found that neuronal spiking in area MT is better than bursting at predicting motion detection in macaques (p < 0.01). Conclusions: Therefore, our results suggest that area MT neurons do not fire behaviourally meaningful bursts in response to coherent motion. This finding is useful for learning about the visual processing pathway, and how information is coded in the brain. Limitations: A key limitation of this study is that we did not exclude any experiments from analysis to control for quality of the collected data, perhaps leading to confounding factors.
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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 | 0.005 |
| 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) | 0.001 | 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".