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Record W2547553475 · doi:10.2741/s472

The dendritic tree a mathematical integrator

2016· review· en· W2547553475 on OpenAlex
Lyes Bachatene

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Bioscience-Scholar · 2016
Typereview
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsNeuroscienceStimulus (psychology)Visual cortexExcitatory postsynaptic potentialSummationInhibitory postsynaptic potentialSurround suppressionIntegratorCortical neuronsComputer scienceSpatial frequencyVisual perceptionPsychologyPhysicsStimulationCognitive psychologyPerceptionOptics

Abstract

fetched live from OpenAlex

Neurons in the primary visual cortex (V1) are sensitive to simple features of the visual scene such as contrast, spatial frequency or orientations. In higher mammals, they are organized into columns of orientation-preference, whereas such organization is absent in rodents. However, in both types of organization, neurons can be highly selective or poorly selective for a particular stimulus. In mouse V1, it has been shown that all stimuli are represented on the dendritic tree of single neurons. To what extent this concept is applicable in higher mammals? In this review, we discuss possible models of integrating visual information from visual cortical neurons. In particular, how the modulation of the number of inputs and/or the frequency firing can explain the orientation selectivity in V1. Based on our findings and literature, we propose three different hypotheses namely the spatial summation, the temporal summation and the excitation-inhibition. In addition, we discuss the possible interactions between excitatory pyramidal neurons and inhibitory interneurons during stimulus processing.

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.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.002
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 head0.040
GPT teacher head0.299
Teacher spread0.259 · 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