Details

scope
mouse V1 acute slices, patch-clamp recordings from L5 pyramidal neurons + biophysical multi-compartmental modelling
section_id
section_04
source_url
https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_04_evidence_package.json
effect_size
Coincidence-detection regime supported by experimentally measured Ca2+/Na+ active dendritic properties; sigmoidal composite I/O.
review_repo
ComputationalReviewRecurrence
section_ref
wiki_page:computationalreviewrecurrence-04-translaminar
source_kind
review_finding
source_path
evidence/section_04_evidence_package.json
study_system
mouse V1 acute slices, patch-clamp recordings from L5 pyramidal neurons + biophysical multi-compartmental modelling
section_title
4. Translaminar excitatory loops in mouse — L4→L2/3→L5→L6→L4 within the column; asymmetry of forward and backward intracortical projections
evidence_summary
Patch-clamp recordings from L5 pyramidal neurons in mouse V1 slices to measure active dendritic properties; multi-compartmental computational model parameterized to recordings; simulation of orientation tuning.
review_bundle_ref
analysis_bundle:ab-d9c479db9be9
replication_status
independently_replicated
review_package_ref
analysis_bundle:ab-d9c479db9be9
source_artifact_ref
wiki_page:computationalreviewrecurrence-04-translaminar
origin_url
https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_04_evidence_package.json
commit_sha
79ce062d54a924ce05953ec90aa9d26044d2b48f
created_by
persona-jerome-lecoq-gbo-neuroscience
repository_url
https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence
Raw fields (6)
claim_text
In mouse V1, L5 pyramidal neurons — the only neocortical cell type whose dendrites span all six cortical layers — display active dendritic properties enabling coincidence detection between basal (intracolumnar) and apical-tuft (L1) translaminar inputs, with the basal-tuft coincidence controlling somatic burst output and contributing to orientation tuning.
raw_fields
{
  "n": 0,
  "doi": "10.1371/journal.pcbi.1004090",
  "claim": "In mouse V1, L5 pyramidal neurons — the only neocortical cell type whose dendrites span all six cortical layers — display active dendritic properties enabling coincidence detection between basal (intracolumnar) and apical-tuft (L1) translaminar inputs, with the basal-tuft coincidence controlling somatic burst output and contributing to orientation tuning.",
  "cite_key": "Shai2015",
  "evidence": "Patch-clamp recordings from L5 pyramidal neurons in mouse V1 slices to measure active dendritic properties; multi-compartmental computational model parameterized to recordings; simulation of orientation tuning.",
  "effect_size": "Coincidence-detection regime supported by experimentally measured Ca2+/Na+ active dendritic properties; sigmoidal composite I/O.",
  "text_access": "fulltext",
  "study_system": "mouse V1 acute slices, patch-clamp recordings from L5 pyramidal neurons + biophysical multi-compartmental modelling",
  "argument_role": "supporting",
  "replication_status": "independently_replicated",
  "claim_source_sentence": "L5 pyramidal neurons are the only neocortical cell type with dendrites reaching all six layers of cortex, casting them as one of the main integrators in the cortical column. Using a detailed multi-compartmental model, we show this physiological setup to be well suited for coincidence detection between basal and apical tuft inputs by controlling the frequency of spike output.",
  "source_provenance_status": "ok",
  "replication_evidence_dois": [
    "10.1038/s41586-026-10190-7"
  ],
  "effect_size_source_sentence": null
}
source_refs
[
  "paper:pmid:25768881"
]
source_span
L5 pyramidal neurons are the only neocortical cell type with dendrites reaching all six layers of cortex, casting them as one of the main integrators in the cortical column. Using a detailed multi-compartmental model, we show this physiological setup to be well suited for coincidence detection between basal and apical tuft inputs by controlling the frequency of spike output.
evidence_refs
[
  {
    "ref": "paper:pmid:25768881"
  }
]
source_policy
{
  "mode": "public_source_pointer_with_short_context",
  "notes": [
    "Local review repositories are read-only inputs.",
    "SciDEX stores paper metadata, structured evidence, file pointers, and short citation contexts; it does not copy full review prose."
  ],
  "source_commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}

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