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Record W4317937270 · doi:10.1016/j.celrep.2023.112030

Hierarchical retinal computations rely on hybrid chemical-electrical signaling

2023· article· en· W4317937270 on OpenAlexafffund
Laura Hanson, Prathyusha Ravi-Chander, David M. Berson, Gautam B. Awatramani

Bibliographic record

VenueCell Reports · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsNeuroscienceGap junctionRetinalElectrical SynapsesRetinaCoupling (piping)Biological neural networkRetinal ganglion cellGlutamate receptorBiologyOrientation (vector space)ChemistryCell biologyMaterials scienceBiochemistryIntracellularReceptor

Abstract

fetched live from OpenAlex

Bipolar cells (BCs) are integral to the retinal circuits that extract diverse features from the visual environment. They bridge photoreceptors to ganglion cells, the source of retinal output. Understanding how such circuits encode visual features requires an accounting of the mechanisms that control glutamate release from bipolar cell axons. Here, we demonstrate orientation selectivity in a specific genetically identifiable type of mouse bipolar cell-type 5A (BC5A). Their synaptic terminals respond best when stimulated with vertical bars that are far larger than their dendritic fields. We provide evidence that this selectivity involves enhanced excitation for vertical stimuli that requires gap junctional coupling through connexin36. We also show that this orientation selectivity is detectable postsynaptically in direction-selective ganglion cells, which were not previously thought to be selective for orientation. Together, these results demonstrate how multiple features are extracted by a single hierarchical network, engaging distinct electrical and chemical synaptic pathways.

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)0.000
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 score0.015
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.240
Teacher spread0.230 · 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

Citations30
Published2023
Admission routes2
Has abstractyes

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