Functional implications of compartment-specific homeostatic regulation and the feasibility of independent local feedback signals
Bibliographic record
Abstract
ABSTRACT Neurons regulate their average firing rate by adjusting their synaptic strength and intrinsic excitability. Intracellular calcium is implicated as an error signal for both types of homeostatic regulation. Past studies have focused on global calcium but two properties cannot be independently regulated by one error signal. Here, we computationally tested the implications of local vs global feedback and the feasibility of local (compartment-specific) error signals based on spatially segregated calcium changes. Simulations in a simple two-compartment model confirmed that a perturbation applied to one compartment induces local compensation only if feedback is compartment-specific. Simulations in a biophysically detailed multicompartment model with realistic calcium handling confirmed that dendritic and somatic calcium signals remain relatively segregated and can, therefore, encode separate error signals. Strong perturbations (as often tested experimentally) triggered widespread compensation because local compensation was overwhelmed. Non-local compensation also occurred when the spatial segregation of calcium signals was weakened. Our results demonstrate the plausibility of compartment-specific feedback using calcium-based error signals. Furthermore, whereas local homeostatic regulation nullifies local perturbations through compensation within the affected compartment, non-local regulation causes widespread compensatory changes that, while restoring the neuron’s overall input-output relationship, distorts the input-output relationship of individual compartments, with potentially important consequences.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".