Somatostatin Interneurons Recruit Pre- and Postsynaptic GABA<sub>B</sub>Receptors in the Adult Mouse Dentate Gyrus
Bibliographic record
Abstract
The integration of spatial information in the mammalian dentate gyrus (DG) is critical to navigation. Indeed, DG granule cells (DGCs) rely upon finely balanced inhibitory neurotransmission in order to respond appropriately to specific spatial inputs. This inhibition arises from a heterogeneous population of local GABAergic interneurons (INs) that activate both fast, ionotropic GABAAreceptors (GABAAR) and slow, metabotropic GABABreceptors (GABABR), respectively. GABABRs in turn inhibit pre- and postsynaptic neuronal compartments via temporally long-lasting G-protein-dependent mechanisms. The relative contribution of each IN subtype to network level GABABR signal setting remains unknown. However, within the DG, the somatostatin (SSt) expressing IN subtype is considered crucial in coordinating appropriate feedback inhibition on to DGCs. Therefore, we virally delivered channelrhodopsin 2 to the DG in order to obtain control of this specific SSt IN subpopulation in male and female adult mice. Using a combination of optogenetic activation and pharmacology, we show that SSt INs strongly recruit postsynaptic GABABRs to drive greater inhibition in DGCs than GABAARs at physiological membrane potentials. Furthermore, we show that in the adult mouse DG, postsynaptic GABABR signaling is predominantly regulated by neuronal GABA uptake and less so by astrocytic mechanisms. Finally, we confirm that activation of SSt INs can also recruit presynaptic GABABRs, as has been shown in neocortical circuits. Together, these data reveal that GABABR signaling allows SSt INs to control DG activity and may constitute a key mechanism for gating spatial information flow within hippocampal circuits.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".