Distinct Neural Mechanisms of Visual and Sound Adaptation in the Cat Visual Cortex
Bibliographic record
Abstract
Sensory areas exhibit modular selectivity to stimuli, but they can also respond to features outside of their basic modality. Several studies have shown cross-modal plastic modifications between visual and auditory cortices; however, the exact mechanisms of these modifications are yet not completely known. To this aim, we investigated the effect of 12 min of visual versus sound adaptation (referring to forceful application of an optimal/nonoptimal stimulus to a neuron[s] under observation) on the infragranular and supragranular primary visual neurons (V1) of the cat (Felis catus). Previous reports showed that both protocols induced orientation tuning shifts, but sound increased the bandwidths. Here, we compared visual versus sound adaptation effects, specifically analysing the firing changes and variability (computed as Fano factor) for raw and centred (around optimal orientation) tuning curves. We report that, compared with visual adaptation, sound adaptation elicited broader tuning curves in supragranular and infragranular layers accompanied with decreased variability in both cortical layers. This decreased variability may reflect stabilization of neural responses through enhanced inhibitory control or synaptic efficacy in local circuits. These findings suggest unique modulation of neural responses by distinct adaptation protocols, resulting in disparate tunings. We suggest that broader tuning curves and decreased response variability after sound adaptation may keep the visual cortex prepared across a spectrum of abstract representations that match with visual stimuli.
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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 | 0.000 |
| 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) | 0.000 | 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".