Modulation of the synchronization between cells in visual cortex by contextual targets
Bibliographic record
Abstract
It has been suggested that synchronization of action potentials encodes diverse features of a single image. However, properties of the synchronization, which occurs on a time scale of approximately 1-5 ms, are still poorly understood. We have tested the modulation of synchronization by manipulating the contextual targets introduced in the surround of the receptive field. Experiments were carried out on anaesthetized cats prepared for multiunit and single-cell recordings in area 17. Initially, a patch of sine-wave drifting grating was positioned over the overlapping receptive fields of several neurons. If this coherent motion produced a significant synchronization in cross-correlograms, contextual targets were added. The first contextual stimuli were two sine-wave patches placed above and below the central compound receptive field. Only the contrast of contextual targets changed. Results show that the larger the differential contrast the higher the synchronization. The second contextual stimulus was a lateral shift of a sine-wave patch. Data show that the wider the distance between the central and peripheral patches the better the synchronization. Furthermore, results suggest that the synchrony pattern computed by cross correlating multiunit recordings from two sites differs when the cross correlation is carried out between individual units belonging to each multiunit recording. Together with our previous results it appears that synchronization is stimulus dependent and its strength increases with larger disparities included in the whole stimulating image.
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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 | 0.001 |
| 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".