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Record W4401710996 · doi:10.1016/j.celrep.2024.114590

Optogenetic silencing of medial septal GABAergic neurons disrupts grid cell spatial and temporal coding in the medial entorhinal cortex

2024· article· en· W4401710996 on OpenAlexafffund
Jennifer C. Robinson, Johnson Ying, Michael E. Hasselmo, Mark P. Brandon

Bibliographic record

VenueCell Reports · 2024
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsMcGill University Health CentreMcGill UniversityDouglas Mental Health University Institute
FundersNational Institute of Mental HealthOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsNeuroscienceOptogeneticsHippocampal formationEntorhinal cortexGABAergicHippocampusGrid cellBiologySpatial memoryPopulationInhibitory postsynaptic potentialGridWorking memoryMedicineCognition

Abstract

fetched live from OpenAlex

The hippocampus and medial entorhinal cortex (MEC) form a cognitive map that facilitates spatial navigation. As part of this map, MEC grid cells fire in a repeating hexagonal pattern across an environment. This grid pattern relies on inputs from the medial septum (MS). The MS, and specifically GABAergic neurons, are essential for theta rhythm oscillations in the entorhinal-hippocampal network; however, the role of this population in grid cell function is unclear. To investigate this, we use optogenetics to inhibit MS-GABAergic neurons and observe that MS-GABAergic inhibition disrupts grid cell spatial periodicity. Grid cell spatial periodicity is disrupted during both optogenetic inhibition periods and short inter-stimulus intervals. In contrast, longer inter-stimulus intervals allow for the recovery of grid cell spatial firing. In addition, grid cell phase precession is also disrupted. These findings highlight the critical role of MS-GABAergic neurons in maintaining grid cell spatial and temporal coding in the MEC.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

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

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 head0.038
GPT teacher head0.283
Teacher spread0.245 · 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 teacher head, 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

Citations12
Published2024
Admission routes2
Has abstractyes

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