Long-term Potentiation Induces Expanded Movement Representations and Dendritic Hypertrophy in Layer V of Rat Sensorimotor Neocortex
Bibliographic record
Abstract
While long-term potentiation (LTP) is currently the most widely investigated model of the synaptic mechanisms underlying learning, there is a paucity of reports on the direct effects of LTP on cortical organization. Here we show that strengthening polysynaptic potentiation correlates with an expanded neocortical area that responds to intracortical microstimulation-induced movements of rat forelimb and increased dendritic material in layer V pyramidal cells. Rats carried a stimulating electrode in the corpus callosum (midline), and a recording electrode in the right caudal forelimb area (CFA). Each rat received 15 days of either high frequency stimulation (HFS) or handling. Evoked potentials of the transcallosal pathway were recorded in the right hemisphere before and after 15 days of stimulation or handling. Following the last stimulation, movement representations were determined in the left CFA using high-resolution intracortical microstimulation (ICMS) and then the brains were processed for Golgi-Cox staining. Our results show that synaptic modification results in a recruitment of more neocortical area into movement representations and increases in several measures of dendritic morphology in layers III and V. This study sheds light on the interaction between artificial models of learning, receptive field characteristics and dendritic morphology in the sensorimotor cortex.
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 | 0.000 |
| 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".