Dendritic distributions of l<sub>h</sub> channels in experimentally-derived multi-compartment models of oriens-lacunosum/moleculare (O-LM) hippocampal interneurons
Bibliographic record
Abstract
The O-LM cell type mediates feedback inhibition onto hippocampal pyramidal cells and gates information flow in the CA1. Its functions depend on the presence of voltage-gated channels (VGCs), which affect its integrative properties and response to synaptic input. Given the challenges associated with determining densities and distributions of VGCs on interneuron dendrites, we take advantage of computational modeling to consider different possibilities. In this work, we focus on hyperpolarization-activated channels (h-channels) in O-LM cells. While h-channels are known to be present in O-LM cells, it is unknown whether they are present on their dendrites. In previous work, we used ensemble modeling techniques with experimental data to obtain insights into potentially important conductance balances. We found that the best O-LM models that included uniformly distributed h-channels in the dendrites could not fully capture the “sag” response. This led us to examine activation kinetics and non-uniform distributions of h-channels in the present work. In tuning our models, we found that different kinetics and non-uniform distributions could better reproduce experimental O-LM cell responses. In contrast to CA1 pyramidal cells where higher conductance densities of h-channels occur in more distal dendrites, decreasing conductance densities of h-channels away from the soma were observed in O-LM models. Via an illustrative scenario, we showed that having dendritic h-channels clearly speeds up back-propagating action potentials in O-LM cells, unlike when h-channels are present only in the soma. Although the present results were morphology-dependent, our work shows that it should be possible to determine the distributions and characteristics of O-LM cells with recordings and morphologies from the same cell. We hypothesize that h-channels are distributed in O-LM cell dendrites and endow them with particular synaptic integration properties that shape information flow in hippocampus.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".