Training-induced plasticity in the visual cortex of adult rats following visual discrimination learning
Bibliographic record
Abstract
Changes in synaptic efficacy, including long-term potentiation (LTP) and long-term depression (LTD), provide mechanisms for experience-induced plasticity and play a key role in learning processes. Some types of learning (e.g., motor learning, fear conditioning) result in LTP and/or LTD-like changes at synapses. Here, rats learned to discriminate two visual stimuli, P+ and P-, indicating the presence and absence, respectively, of a hidden escape platform in a Y-shaped water maze. Following task acquisition, trained rats showed larger amplitude of visually evoked potentials (VEPs) in V1 to both stimuli encountered during training relative to novel stimuli. Training also resulted in a facilitation of LTP induced by theta-burst stimulation (TBS) of thalamic afferents to V1 with no effect on depression induced by low-frequency stimulation (LFS). Visual VEP enhancement and increased LTP both required that visual stimuli carried some significance to the animal, as both effects were absent in control rats exposed to the same visual stimuli in the absence of pairing with platform location. Together, these experiments show that visual experience can result in a stimulus-selective response enhancement and an increase in the synaptic modification range of V1 synapses, providing a novel example of metaplasticity in circuits of the adult cortex.
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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) | 0.001 | 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 | 0.001 |
| 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".