A receptor-inactivation model for single-celled habituation in Stentor coeruleus
Bibliographic record
Abstract
The single-celled ciliate Stentor coeruleus demonstrates habituation to mechanical stimuli, but the mechanism of learning in this single cell, which lacks a nervous system, is currently not known. Here, we propose a simple biochemistry-based model based on prior electrophysiological measurements in Stentor along with general properties of receptor molecules. In this model, a mechanoreceptor senses the stimulus, which leads to channel opening to change membrane potential, with a sufficient change in polarization triggering an action potential that drives contraction. Receptors that are activated can become internalized, after which they can either be degraded or recycled back to the cell surface. Simulations of this model confirm that it is capable of showing habituation similar to what is seen in actual Stentor cells, including the apparently step-like response of individual cells during habituation. The model also can account for additional habituation hallmarks, including the dependence of habituation rate on stimulus magnitude and the ability of high-frequency stimulus sequences to drive faster and more extensive habituation. The model makes the prediction that application of high-force stimuli that do not normally habituate should drive habituation to weaker stimuli due to a decrease in the receptor numbers, which serves as an internal hidden variable. We confirmed this prediction using two new sets of experiments involving the alternation of weak and strong stimuli. The model also predicts subliminal accumulation, wherein continuation of training, even after habituation has reached asymptotic levels, should lead to delayed response recovery, which was also confirmed by new experiments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".