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 GABA A receptors (GABA A R) and slow, metabotropic GABA B receptors (GABA B R), respectively. GABA B Rs 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 GABA B R 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 GABA B Rs to drive greater inhibition in DGCs than GABA A Rs at physiological membrane potentials. Furthermore, we show that in the adult mouse DG, postsynaptic GABA B R 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 GABA B Rs, as has been shown in neocortical circuits. Together, these data reveal that GABA B R signaling allows SSt INs to control DG activity and may constitute a key mechanism for gating spatial information flow within hippocampal circuits.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".