Synaptic mechanisms underlying the network state-dependent recruitment of VIP-expressing interneuron-specific interneurons in the CA1 hippocampus
Bibliographic record
Abstract
SUMMARY In the hippocampus, a highly specialized population of vasoactive intestinal peptide (VIP)-expressing interneuron-specific (IS) inhibitory cells provides local circuit disinhibition via preferential innervation of different types of GABAergic interneurons. While disinhibition can be critical in modulating network activity and different forms of hippocampal learning, the synaptic and integrative properties of IS cells and their recruitment during network oscillations remain unknown. Using a combination of patch-clamp recordings, photostimulation, computational modelling as well as recordings of network oscillations simultaneously with two-photon Ca 2+ -imaging in awake mice in vivo , we identified synaptic mechanisms that can control the firing of IS cells, and explored their impact on the cell recruitment during theta oscillations and sharp-wave-associated ripples. We found that IS cells fire spikes in response to both the Schaffer collateral and the temporoammonic pathway activation. Moreover, integrating their intrinsic and synaptic properties into computational models predicted recruitment of these cells during the rising to peak phases of theta oscillations and during ripples depending on inhibitory contributions. In vivo Ca 2+ -imaging in awake mice confirmed in part the theoretical predictions, revealing a significant speed modulation of IS cells and their preferential albeit delayed recruitment during theta-run epochs, with firing at the rising phase to peak of the theta cycle. However, it also uncovered that IS cells are not activated during ripples. Thus, given the preferential theta-modulated firing of IS cells in awake hippocampus, we postulate that these cells may be important for information gating during spatial navigation and memory encoding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".