The Rates of Protein Synthesis and Degradation Account for the Differential Response of Neurons to Spaced and Massed Training Protocols
Bibliographic record
Abstract
The sensory-motor neuron synapse of Aplysia is an excellent model system for investigating the biochemical changes underlying memory formation. In this system, training that is separated by rest periods (spaced training) leads to persistent changes in synaptic strength that depend on biochemical pathways that are different from those that occur when the training lacks rest periods (massed training). Recently, we have shown that in isolated sensory neurons, applications of serotonin, the neurotransmitter implicated in inducing these synaptic changes during memory formation, lead to desensitization of the PKC Apl II response, in a manner that depends on the method of application (spaced versus massed). Here, we develop a mathematical model of this response in order to gain insight into how neurons sense these different training protocols. The model was developed incrementally, and each component was experimentally validated, leading to two novel findings: First, the increased desensitization due to PKA-mediated heterologous desensitization is coupled to a faster recovery than the homologous desensitization that occurs in the absence of PKA activity. Second, the model suggests that increased spacing leads to greater desensitization due to the short half-life of a hypothetical protein, whose production prevents homologous desensitization. Thus, we predict that the effects of differential spacing are largely driven by the rates of production and degradation of proteins. This prediction suggests a powerful mechanism by which information about time is incorporated into neuronal processing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".