Calcium-dependent subthreshold fluctuations in membrane voltage; a modeling study
Bibliographic record
Abstract
We have produced a stochastic computer model that incorporates K Ca channels and calcium dynamics into a CA3 pyramidal neuron, which is based on the biophysically realistic Traub model [ 3 ]. To introduce channel noise, we replaced all Hodgkin-Huxley (HH) channels with equivalent Markov models [ 4 ]. There is also an intracellular calcium pool, with Ca levels that vary stochastically due to influx through Markovian calcium channels. Preliminary simulation results show that, for the default parameters used by Traub, there is anti-correlation between intracellular calcium and membrane voltage (Figure 1 ); this suggests intracellular calcium fluctuations may partially drive low-frequency voltage noise. Additional modeling has implicated the Ca -dependent afterhyperpolarization current (I AHP ) as the primary linkage between these two signals. Power spectrum analysis suggests that the contribution of intracellular calcium fluctuations is dominant at low frequencies, below the natural cutoff for I AHP noise (Figure 2 ). We believe that this linkage between membrane potential noise and intracellular calcium could regulate many of the well-documented roles of noise in the nervous system [ 1 ]. Relationship between intracellular calcium and membrane voltage. Intracellular calcium (blue, arb units) is inverted and scaled to show anti-correlation with membrane voltage (green). (Correlation coefficient -56.4%, Vm phase lag ~600ms, Traub model default parameters.) Power spectral density for stochastic I AHP current Black trace is simulation under default settings. Red trace shows the effects of clamping intracellular calcium, which reduces low-frequency power. Green trace shows the I AHP current when inherent thermal fluctuations are removed by switching channel dynamics from Markov to HH. The crossover of these two signals suggests that intracellular calcium fluctuations can contribute to low-frequency voltage noise.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".