Subtractive and Divisive Inhibition: Effect of Voltage-Dependent Inhibitory Conductances and Noise
Bibliographic record
Abstract
The influence of voltage-dependent inhibitory conductances on firing rate versus input current (f-I) curves is studied using simulations from a new compartmental model of a pyramidal cell of the weakly electric fish Apteronotus leptorhynchus. The voltage dependence of shunting-type inhibition enhances the subtractive effect of inhibition on f-I curves previously demonstrated in Holt and Koch (1997) for the voltage-independent case. This increased effectiveness is explained using the behavior of the average subthreshold voltage with input current and, in particular, the nonlinearity of Ohm's law in the subthreshold regime. Our simulations also reveal, for both voltage-dependent and -independent inhibitory conductances, a divisive inhibition regime at low frequencies (f < 40 Hz). This regime, dependent on stochastic inhibitory synaptic input and a coupling of inhibitory strength and variance, gives way to subtractive inhibition at higher-output frequencies (f > 40 Hz). A simple leaky integrate-and-fire type model that incorporates the voltage dependence supports the results from our full ionic simulations.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".