Neural Activity Profiles of the Neocortex and Superior Colliculus after Bimodal Sensory Stimulation
Bibliographic record
Abstract
Current efforts at functional mapping of multisensory neurons are hampered by the need for both cellular-level resolution and the separate visualization of activity by different sensory cues. We have used a recently developed technique that exploits the differential time course of zif268 mRNA versus protein induction in neurons after sensory stimulation. Adult male rats were visually and acoustically deprived and then exposed to one of the following stimulation sequences: (i) no sensory stimulation; (ii) 2 h visual stimulation followed by 30 min auditory stimulation; (iii) 2 h auditory stimulation followed 30 min of visual stimulation; and (iv) 2 h compound visual and auditory stimulation. The neocortex and superior colliculus (SC) were then processed for fluorescent immunocytochemistry and in situ hybridization for staining of Zif268 protein and mRNA products. We have found that activity patterns in primary visual and auditory cortices were in accord with the sequence of the compound stimulus. We also show that SC superficial layers contained a pool of exclusively unimodal neurons, similar to that of visual cortex. Activity patterns of deep SC layers contained multimodal neurons with varying degrees of visual and auditory convergence. The deep SC layers also showed that auditory processing was largely carried out by a small, bimodal group of neurons whereas visual processing was coordinated by both a large unimodal and a small bimodal pool of neurons.
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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) | 0.001 | 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".