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Record W4289977503 · doi:10.1101/2022.08.04.502628

The medial entorhinal cortex is necessary for the stimulus control over hippocampal place fields by distal, but not proximal, landmarks

2022· preprint· en· W4289977503 on OpenAlexaff
Elizabeth A. M. A. Allison, Joe W. Moore, Daisy Arkell, Paul A. Dudchenko, Emma R. Wood

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsDiscovery Centre
FundersBiotechnology and Biological Sciences Research CouncilDirectorate for Biological Sciences
KeywordsEntorhinal cortexNeuroscienceHippocampal formationIbotenic acidHippocampusStimulus (psychology)AnatomyPsychologyBiologyCentral nervous systemCognitive psychology

Abstract

fetched live from OpenAlex

Abstract A fundamental property of place cells in the hippocampus is the anchoring of their firing fields to salient landmarks within the environment. However, it is unclear how such information reaches the hippocampus. In the current experiment, we tested the hypothesis that the stimulus control exerted by distal visual landmarks requires input from the medial entorhinal cortex (MEC). Place cells were recorded from mice with ibotenic acid lesions of the MEC (n = 7) and from sham-lesioned mice (n = 6) following 90° rotations of either distal landmarks or proximal cues in a cue controlled environment. We found that lesions of the MEC impaired the anchoring of place fields to distal landmarks, but not proximal cues. We also observed that, relative to sham-lesioned mice, place cells in animals with MEC lesions exhibited significantly reduced spatial information and increased sparsity. These results support the view that distal landmark information reaches the hippocampus via the MEC, but that proximal cue information can do so via an alternative neural pathway.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.026
GPT teacher head0.261
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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