NMDA Receptor Mediated Plasticity in Learning and Memory
Bibliographic record
Abstract
Abstract One view of the organization of learning and memory functions in the mammalian brain is that there are multiple learning and memory networks that acquire and store different kinds of information. Each neural network is thought to have a central structure. The hippocampus, amygdala, perirhinal cortex, and dorsal striatum are thought to be central structures for different learning and memory networks important for spatial/relational, emotional, visual objects, and instrumental memory respectively. These central structures are part of a complex network including cortical and subcortical brain regions containing areas important for sensory, motivational, modulatory, and output functions. These networks are thought to encode and store information obtained during experiences via a general plasticity mechanism in which the relationship between synapses in these regions are changed. This view suggests that that memory has a physical manifestation in the brain, which allows for synapses to communicate more effectively as a result of activation. One form of synaptic plasticity called long-term potentiation (LTP) is considered a fundamental form of changes in synaptic efficacy mediating learning and long-term memory functions. One of the biochemical mechanisms for initiating LTP is triggered when a type of glutamate receptor, N-methyl-D-aspartate receptor (NMDAR), found in all of these memory networks is activated and various biochemical pathways that can produce long-term enhancements to the efficacy of that synapse are recruited. NMDAR-mediated LTP processes appear to be important for learning and memory processes in these different networks, but there are clear differences. None of the networks require NMDAR functions during expression of new learning. All the networks required NMDAR function during encoding of new information, except the network centered on perirhinal cortex. Finally, all of the networks required NMDAR-mediated plasticity processes for long-term consolidation of new information, except the one centered on the amygdala.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".