Modeling the Influence of Synaptic Plasticity on After-effects
Bibliographic record
Abstract
While circadian rhythms in physiology and behavior demonstrate remarkable day-to-day precision, they are also able to exhibit plasticity in a variety of parameters and under a variety of conditions. After-effects are one type of plasticity in which exposure to non-24-h light-dark cycles (T-cycles) will alter the animal's free-running rhythm in subsequent constant conditions. We use a mathematical model to explore whether the concept of synaptic plasticity can explain the observation of after-effects. In this model, the SCN is composed of a set of individual oscillators randomly selected from a normally distributed population. Each cell receives input from a defined set of oscillators, and the overall period of a cell is a weighted average of its own period and that of its inputs. The influence that an input has on its target's period is determined by the proximity of the input cell's period to the imposed T-cycle period, such that cells with periods near T will have greater influence. Such an arrangement is able to duplicate the phenomenon of after-effects, with relatively few inputs per cell (~4-5) being required. When the variability of periods between oscillators is low, the system is quite robust and results in minimal after-effects, while systems with greater between-cell variability exhibit greater magnitude after-effects. T-cycles that produce maximal after-effects have periods within ~2.5 to 3 h of the population period. Overall, this model demonstrates that synaptic plasticity in the SCN network could contribute to plasticity of the circadian period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".