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Record W2795117877 · doi:10.1002/hipo.22843

Relocating cued goals induces population remapping in CA1 related to memory performance in a two‐platform water task in rats

2018· article· en· W2795117877 on OpenAlexafffund
Justin Q. Lee, Deryn O. LeDuke, Kate Chua, Robert J. McDonald, Robert J. Sutherland

Bibliographic record

VenueHippocampus · 2018
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsQuest University CanadaUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Lethbridge
KeywordsEncoding (memory)Task (project management)Cued speechNeurosciencePopulationHippocampusPsychologyRepresentation (politics)Cued recallCognitive psychologyComputer scienceCognitionFree recall

Abstract

fetched live from OpenAlex

The activity of CA1 neurons in the rodent hippocampus represents multiple aspects of learning episodes, including cue and place information. Previous reports on cue and place representation in CA1 have examined activity in single neurons and population recordings during free exploration of an environment or when actions are directed to either cue or place aspects of memory tasks. To better understand cue and place memory representation in CA1, and how these interact during goal-directed navigation, we investigated population activity in CA1 during memory encoding and retrieval in a novel water task with two visibly distinct platforms, using mRNA for immediate early genes Arc and Homer1a as markers of neural activity. After training, relocating cues to new places induces an extensive, perhaps global, remapping of the memory code that is accompanied by altered navigation and rapid learning of new cue-place information. In addition, we have found a significant relationship between the extent of reactivation and overall cue choice accuracy. These findings demonstrate an important relationship between population remapping in CA1 and memory-guided behavior.

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 score0.012
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.056
GPT teacher head0.318
Teacher spread0.262 · 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

Citations11
Published2018
Admission routes2
Has abstractyes

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