Long-Term Depression and Depotentiation in the Sensorimotor Cortex of the Freely Moving Rat
Bibliographic record
Abstract
Activity-dependent reductions in synaptic efficacy are central components of recent models of cortical learning and memory. Here, we have examined long-term synaptic depression (LTD) and the reversal of long-term potentiation (depotentiation) of field potentials evoked in sensorimotor cortex by stimulation of the white matter in the adult, freely moving rat. Prolonged, low-frequency stimulation (1 Hz for 15 min) was used to induce either depotentiation or LTD. LTD was expressed as a reduction in the amplitude of both monosynaptic and polysynaptic field potential components. Both LTD and depotentiation were reliably induced by stimulation of the ipsilateral white matter. Stimulation of the contralateral neocortex induced only a depotentiation effect, which decayed more rapidly than that induced by ipsilateral stimulation (hours vs days). Although ipsilateral LTD was effectively induced by a single session of low-frequency stimulation, multiple sessions of stimulation, either massed or spaced, induced LTD effects that were larger in magnitude and longer lasting. Previously, we showed that the induction of long-term potentiation in the neocortex of chronic preparations required multiple, spaced stimulation sessions to reach asymptotic levels. Here, we report that LTD also required multiple stimulation sessions to reach asymptotic levels, but massed and spaced patterns of low-frequency stimulation were equally effective.
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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".