Bibliographic record
Abstract
Abstract Synaptic plasticity, the ability of chemical synapses to strengthen or weaken, has long been postulated to be a mechanistic basis of memory. Long-term potentiation (LTP), one form of synaptic plasticity, is defined as a persistent increase in the strength of synaptic transmission, whereas long-term depression (LTD) is the opposite—a persistent decrease in the strength of synaptic transmission. Both LTP and LTD are typically induced artificially with trains of electric stimulation applied to presynaptic neurons, and the resulting change in postsynaptic strength is monitored over time. At the receptor level, there are forms of LTP and LTD that are dependent on the neurotransmitter glutamate activating the N-methyl-d-aspartate (NMDA) receptor. This is important because the NMDA receptor can be thought of as the physical instantiation of the Hebb learning rule. It serves as a coincidence detector, as it becomes active when sufficient input from a presynaptic neuron causes the postsynaptic neuron to fire an action potential. There are also non-glutamate and non-NMDA forms of both LTP and LTD. How LTP and LTD relate to memory in awake behaving animals has been examined three different ways: (a) demonstrations of changes in synaptic physiology due to memory formation (learning), (b) attempts to interfere with memory formation by saturating plasticity using stimulation protocols that induce LTP or LTD, and (c) attempts to block memory formation and LTP or LTD by pharmacological or genetic manipulations. There is a plethora of evidence for LTP- and LTD-like mechanisms overlapping with typical memory formation. Following the detailed characterization of LTP and LTD at the molecular, receptor, synaptic, and behavioral levels in nonhuman animal models, clinical researchers began successfully using noninvasive transcranial magnetic stimulation techniques, which result in LTP- and LTD-like changes, in people with substance use, neurological, and psychiatric disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".