Canadian cancer statistics at a glance: cancer in children
Bibliographic record
Abstract
The interplay between hippocampus and medial entorhinal cortex (mEC) is of key importance for forming spatial representations. Within the hippocampal-entorhinal loop, the hippocampus receives context-specific signals from layers II/III of the mEC and feeds memory-associated activity back into layer V (LV). The processing of this output signal within the mEC, however, is largely unknown. We characterized the activation of the receiving mEC network by evoked and naturally occurring output patterns in mouse hippocampal-entorhinal cortex slices. Both types of glutamatergic neurons (mEC LVa and LVb) as well as fast-spiking inhibitory interneurons receive direct excitatory input from the intermediate/ventral hippocampus. Connections between the two types of excitatory neurons are sparse, and local processing of hippocampal output signals within mEC LV is asymmetric, favoring excitation of far projecting LVa neurons over locally projecting LVb neurons. These findings suggest a new role for mEC LV as a bifurcation gate for feedforward (telencephalic) and feedback (entorhinal-hippocampal) signal propagation. STATEMENT OF SIGNIFICANCE Patterned network activity in hippocampal networks plays a key role in the formation and consolidation of spatial memories. It is, however, largely unclear how information is transferred to the neocortex for long-term engrams. Here, we elucidate the propagation of network activity from the hippocampus to the medial entorhinal cortex. We show that patterned output from the hippocampus reaches both major cell types of deep entorhinal layers. These cells are, however, only weakly connected, giving rise to two parallel streams of activity for local and remote signal propagation, respectively. The relative weight of both pathways is regulated by local inhibitory interneurons. Our data reveal important insights into the hippocampal-neocortical dialogue which is of key importance for memory consolidation in the mammalian brain.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.075 | 0.010 |
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".