Non-uniform dendritic distributions of Ihchannels in experimentally-derived multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons
Bibliographic record
Abstract
Inhibitory interneurons are crucial for generating prominent network rhythms and coordinating information flow in hippocampal microcircuits. The oriens-lacunosum/moleculare (O-LM) cell is an interneuron type in the hippocampal CA1 region that synapses onto distal dendrites of pyramidal cells [1]. O-LM cells mediate feedback inhibition onto pyramidal cells and gate information flow between sensory input from entorhinal cortex and previously stored associations from the CA3 area. Despite the distal location of inhibitory synapses from O-LM cells onto the excitatory populations, their control of pyramidal cell output has been clearly shown [2]. Thus, how the dynamic firing properties of O-LM cells in its network circuit environment is generated needs to be understood. Furthermore, it is clear that the presence and distribution of voltage-gated channels on the dendrites of O-LM cells would affect its integrative properties in response to synaptic input. However, given the highly challenging aspects to experimentally determine whether and what sort of distributions of voltage-gated channels are present on dendrites, we take advantage of computational modeling studies to consider different possibilities. In this work, we focus on Ih channels. While the existence of Ih channels in O-LM cells has long been known [3], it is at present unknown whether these channels are present on O-LM cell dendrites. In previous work, we used ensemble modeling techniques in conjunction with experimental data to show that physiologically realistic multi-compartment O-LM cell models may possess dendritic Ih, but only uniform distributions across the dendritic tree were examined. In the work here, we turned our focus to how the kinetics of Ih and non-uniform distributions would affect our models’ output. In tuning our models, we found that different Ih kinetics as well as non-uniform distributions were better able to reproduce experimental O-LM cell responses. Interestingly, this occurred only when there were decreasing conductance densities away from the soma. This is in contrast to pyramidal cells which have higher Ih conductance densities in more distal dendrites [4]. Non-uniform distributions of Ih would indicate that there are particular synaptic input distributions that affect the firing properties of O-LM cells and thus their ability to affect information flow.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".