Entorhinal cortex glutamatergic and GABAergic projections bidirectionally control discrimination and generalization of hippocampal representations
Bibliographic record
Abstract
Abstract Discrimination and generalization are crucial brain-wide functions for memory and object recognition that utilize pattern separation and completion computations. Circuit mechanisms supporting these operations remain enigmatic. We show lateral entorhinal cortex glutamatergic (LEC GLU ) and GABAergic (LEC GABA ) projections are essential for object recognition memory. Silencing LEC GLU during in vivo two-photon imaging increased the population of active CA3 pyramidal cells but decreased activity rates, suggesting a sparse coding function through local inhibition. Silencing LEC GLU also decreased place cell remapping between different environments validating this circuit drives pattern separation and context discrimination. Optogenetic circuit mapping confirmed that LEC GLU drives dominant feedforward inhibition to prevent CA3 somatic and dendritic spikes. However, conjunctively active LEC GABA suppresses this local inhibition to disinhibit CA3 pyramidal neuron soma and selectively boost integrative output of LEC and CA3 recurrent network. LEC GABA thus promotes pattern completion and context generalization. Indeed, without this disinhibitory input, CA3 place maps show decreased similarity between contexts. Our findings provide circuit mechanisms whereby long-range glutamatergic and GABAergic cortico-hippocampal inputs bidirectionally modulate pattern separation and completion, providing neuronal representations with a dynamic range for context discrimination and generalization.
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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 | 0.000 |
| 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".