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Record W4404138312 · doi:10.1016/j.neuron.2024.10.014

NMDA receptors regulate the firing rate set point of hippocampal circuits without altering single-cell dynamics

2024· article· en· W4404138312 on OpenAlexfundno aff
Antonella Ruggiero, Leore R. Heim, Lee Susman, Dema Hreaky, Ilana Shapira, Maxim Katsenelson, Kobi Rosenblum, Inna Slutsky

Bibliographic record

VenueNeuron · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsnot available
FundersTel Aviv UniversityBIRAXDeutsche ForschungsgemeinschaftRosetrees TrustAzrieli FoundationPrinceton University
KeywordsHippocampal formationNeuroscienceNMDA receptorSet pointReceptorDynamics (music)HippocampusChemistryPsychology

Abstract

fetched live from OpenAlex

Understanding how neuronal circuits stabilize their activity is a fundamental yet poorly understood aspect of neuroscience. Here, we show that hippocampal network properties, such as firing rate distribution and dimensionality, are actively regulated, despite perturbations and single-cell drift. Continuous inhibition of N-methyl-D-aspartate receptors (NMDARs) ex vivo lowers the excitation/inhibition ratio and network firing rates while preserving resilience to perturbations. This establishes a new network firing rate set point via NMDAR-eEF2K signaling pathway. NMDARs' capacity to modulate and stabilize network firing is mediated by excitatory synapses and the intrinsic excitability of parvalbumin-positive neurons, respectively. In behaving mice, continuous NMDAR blockade in CA1 reduces network firing without altering single-neuron drift or triggering a compensatory response. These findings expand NMDAR function beyond their canonical role in synaptic plasticity and raise the possibility that some NMDAR-dependent behavioral effects are mediated by their unique regulation of population activity set points.

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 score0.026
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.063
GPT teacher head0.319
Teacher spread0.256 · 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

Citations17
Published2024
Admission routes1
Has abstractyes

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