Diffusive Feedback Influences on Hierarchical Information Processing
Bibliographic record
Abstract
In visual information processing, feedforward projection from primary to secondary visual cortex (V1-to-V2) is essential for integrating combinations of oriented bars in order to extract angular information embedded within contours that represent the shape of objects. For feedback (V2-to-V1) projection, two distinct types of pathways have been observed: clustered projection and diffused projection. The former innervates V1 domains with a preferred orientation similar to that of V2 cells of origin. In contrast, the latter innervates without such orientation specificity. V2 cells send their axons to V1 domains with both similar and dissimilar orientation preferences. It is speculated that the clustered feedback projection has a role in contour integration. The role of the diffused feedback projection, however, remains to be seen. We simulated a minimal, functional V1-V2 neural network model. The diffused feedback projection contributed to achieving ongoing-spontaneous subthreshold membrane oscillations in V1 cells, thereby reducing the reaction time of V1 cells to a pair of bars that represents specific angular information. Interestingly, the feedback influence took place even before V2 responses, which might stem largely from ongoing-spontaneous signaling from V2. We suggest that the diffusive feedback influence from V2 could act early in V1 responses and accelerate their reaction speed to sensory stimulation in order to rapidly extract angular information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".