Gain control of firing rate by shunting inhibition: Roles of synaptic noise and dendritic saturation
Bibliographic record
Abstract
Adjusting input-output gain is crucial for information processing by the brain. Gain control of subthreshold depolarization is commonly ascribed to increased membrane conductance caused by shunting inhibition. But contrary to its divisive effect on depolarization, shunting inhibition on its own fails to divisively modulate firing rate, apparently upsetting a critical tenet of neural models that use shunting inhibition to achieve gain control. Using a biophysically realistic neuron model, we show that divisive modulation of firing rate by shunting inhibition requires synaptic noise to smooth the relation between firing rate and somatic depolarization; although necessary, noise alone endows shunting inhibition with only a modest divisive effect on firing rate. In addition to introducing noise, synaptic input is associated with a nonlinear relation between somatic depolarization and excitation because of dendritic saturation; this nonlinearity dramatically enhances divisive modulation of firing rate by shunting inhibition under noisy conditions. Thus, shunting inhibition can act as a mechanism for firing rate gain control, but its modulatory effects (which include both divisive and subtractive components) are fully explained only when both synaptic noise and dendritic saturation are taken into account.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".