Modeling the effects of neuronal morphology on dendritic chloride diffusion and GABAergic inhibition
Bibliographic record
Abstract
Poster presentation at the Twenty Third Annual Computational Neuroscience Meeting: CNS*2014 Québec City, Canada. 26-31 July 2014. Gamma-aminobutyric acid receptors (GABAARs) are ligand-gated chloride (Cl−) channels which mediate the majority of inhibitory neurotransmission in the CNS. Spatiotemporal changes of intracellular Cl− concentration alter the concentration gradient for Cl− across the neuronal membrane and thus affect the current flow through GABAARs and the efficacy of GABAergic inhibition. However, the impact of complex neuronal morphology on Cl− diffusion and the redistribution of intracellular Cl− is not well understood. Recently, computational models for Cl− diffusion and GABAAR-mediated inhibition in realistic neuronal morphologies became available [1-3]. Here we have used computational models of morphologically complex dendrites to test the effects of spines on Cl− diffusion. In all dendritic morphologies tested, spines slowed down longitudinal Cl− diffusion along dendrites and decreased the amount and spatial spread of synaptically evoked Cl− changes. Spine densities of 2-10 spines/µm decreased the longitudinal diffusion coefficient of Cl− to 80-30% of its value in smooth dendrites, respectively. These results suggest that spines are able to limit short-term ionic plasticity [4] at dendritic GABAergic synapses.
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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) | 0.001 | 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 | 0.002 | 0.000 |
| 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".