- claim_text
On the IBL mouse Neuropixels dataset (433 sessions across 270 brain regions), multi-session decoders outperform traditional within-session decoders by exploiting cross-trial and cross-session correlations — establishing both the scale of the standard mouse-cortex recording dataset and the dependency of decoding claims on cross-session structure.
- raw_fields
{
"n": 433,
"doi": "10.1016/j.neuron.2025.10.026",
"claim": "On the IBL mouse Neuropixels dataset (433 sessions across 270 brain regions), multi-session decoders outperform traditional within-session decoders by exploiting cross-trial and cross-session correlations — establishing both the scale of the standard mouse-cortex recording dataset and the dependency of decoding claims on cross-session structure.",
"cite_key": "Zhang2026a",
"evidence": "Quantifies the standardized large-scale mouse-cortex recording resource and shows that decoding gains require leveraging cross-session structure — methodologically relevant for cluster_14.",
"effect_size": "433 sessions; 270 brain regions",
"text_access": "abstract_only",
"study_system": "Mouse brain (cortex and subcortex); IBL Neuropixels dataset",
"argument_role": "supporting",
"replication_status": "single-study",
"claim_source_sentence": "On 433 sessions spanning 270 brain regions in the International Brain Laboratory (IBL) mouse Neuropixels dataset, our decoders outperform traditional approaches on four behaviors, with results generalizing across datasets, species, and tasks.",
"source_provenance_status": "non_substring_match",
"replication_evidence_dois": [],
"effect_size_source_sentence": "On 433 sessions spanning 270 brain regions in the International Brain Laboratory (IBL) mouse Neuropixels dataset, our decoders outperform traditional approaches on four behaviors, with results generalizing across datasets, species, and tasks."
}- source_refs
[
"paper:paper-abc54aa0ef2c"
]
- source_span
On 433 sessions spanning 270 brain regions in the International Brain Laboratory (IBL) mouse Neuropixels dataset, our decoders outperform traditional approaches on four behaviors, with results generalizing across datasets, species, and tasks.
- evidence_refs
[
{
"ref": "paper:paper-abc54aa0ef2c"
}
]- section_title
15. Methodological limits and emerging tools — what current mouse-cortex tools cannot yet measure about E→E recurrence (subthreshold network activity, fast plasticity in vivo, millimetre-scale dynamic connectomes), and what is on the near horizon
- 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"
}