- raw_fields
{
"n": 0,
"doi": "10.1101/2024.02.12.579874",
"claim": "Continuous bump attractor networks for path integration require an explicit error-coding mechanism for ground-truth (landmark) inputs to recalibrate their integration gain, beyond simply correcting position errors.",
"cite_key": "Secer2024",
"evidence": "Theoretical analysis of CBAN architectures plus comparison with hippocampal place-cell experimental data on gain recalibration in mice running on virtual tracks.",
"effect_size": "qualitative",
"text_access": "abstract_only",
"study_system": "continuous bump attractor network model linked to mouse hippocampal place-cell recordings",
"argument_role": "supporting",
"replication_status": "replication_unknown",
"claim_source_sentence": "Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground-truth inputs also fine-tune the gain of the integration process, a crucial factor that links the change in the continuous variable to the updating of the activity bump's location.",
"source_provenance_status": "non_substring_match",
"replication_evidence_dois": [],
"effect_size_source_sentence": null
}- source_refs
[
"paper:paper-862f77f8b74c"
]
- source_span
Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground-truth inputs also fine-tune the gain of the integration process, a crucial factor that links the change in the continuous variable to the updating of the activity bump's location.
- evidence_refs
[
{
"ref": "paper:paper-862f77f8b74c"
}
]- 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"
}