- claim_text
Interareal cortico-cortical pathways in mouse neocortex selectively target specific excitatory and inhibitory neuron populations across different laminae, with feedforward connections terminating most densely in layers 2–4 and feedback projections densest in layer 1, resulting in layer-specific computations that differ in timing and strength of synaptic inputs.
- raw_fields
{
"n": 0,
"doi": "10.3389/fnana.2017.00071",
"claim": "Interareal cortico-cortical pathways in mouse neocortex selectively target specific excitatory and inhibitory neuron populations across different laminae, with feedforward connections terminating most densely in layers 2–4 and feedback projections densest in layer 1, resulting in layer-specific computations that differ in timing and strength of synaptic inputs.",
"cite_key": "DSouza2017",
"evidence": "Review of mouse visual cortical network studies focusing on laminar architecture of interareal communication, hierarchical organization based on axonal termination patterns, excitatory-inhibitory balance, and layer 1 structure and function.",
"effect_size": "",
"text_access": "fulltext",
"study_system": "mouse visual cortex, review",
"argument_role": "supporting",
"replication_status": "consistent_with_prior",
"claim_source_sentence": "A preserved feature across mammals, however, is that feedforward connections terminate most densely in layers 3 and 4. In contrast, feedback projections are densest in layer 1, which is less strongly innervated by local, lateral and feedforward connections (Thomson and Bannister,; Binzegger et al.,; Shipp,).",
"source_provenance_status": "ok",
"replication_evidence_dois": [],
"effect_size_source_sentence": ""
}- source_refs
[
"paper:paper-5a41665f7011"
]
- source_span
A preserved feature across mammals, however, is that feedforward connections terminate most densely in layers 3 and 4. In contrast, feedback projections are densest in layer 1, which is less strongly innervated by local, lateral and feedforward connections (Thomson and Bannister,; Binzegger et al.,; Shipp,).
- evidence_refs
[
{
"ref": "paper:paper-5a41665f7011"
}
]- 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"
}