Maturational Stage-Dependent Contributions of the Cav3.2 T-Type Calcium Channel to Dentate Gyrus Granule Cell Excitability
Bibliographic record
Abstract
T-Type calcium channels shape neuronal excitability driving burst firing, plasticity, and neuronal oscillations that influence circuit activity. The three biophysically distinct T-type channel subtypes (Cav3.1, Cav3.2, Cav3.3) are differentially expressed in the brain, contributing to divergent physiological processes. Cav3.2 channels are highly expressed in the dentate gyrus (DG) of the hippocampus, and mice lacking Cav3.2 [knock-out (KO)] exhibit impairments in hippocampal dependent learning and memory tasks, as well as attenuated development of pilocarpine induced epilepsy. Owing to neurogenesis, granule cells (GCs) are continuously added to the DG, generating a heterogeneous population of maturational stages with distinct excitability. While initial studies identified the role of Cav3.2 in mature GC burst firing, its functional relevance in the intrinsic excitability of different GC subpopulations has not yet been examined. In this study, we used juvenile Cav3.2 KO mice to examine the contributions of Cav3.2 channels to GC excitability at three different stages of maturation. We recorded from cells throughout the GC layer using their electrophysiological and morphological features to allocate GCs into immature, intermediate, and mature groups. In immature GCs, loss of Cav3.2 channels reduced the proportion of cells that fired low-threshold calcium spikes. Conversely, Cav3.2 KO increased excitability in regular spiking intermediate and mature GCs, enabling higher-frequency firing, with little impact on the frequency-dependent response. Overall, this study shows that Cav3.2 channels differentially regulate GC excitability throughout maturation and suggest that calcium influx via Cav3.2 may have maturation-dependent contributions to DG processes such as GC survival, integration, and memory encoding.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".