- claim_text
Classical continuous bump-attractor models suffer from biologically unrealistic fine tuning of recurrent connectivity and from a stereotyped bump shape that cannot encode representational quality; a 2D Amari-type network with locally balanced E/I feedback removes both limitations.
- raw_fields
{
"n": 0,
"doi": "10.1007/s11571-023-09979-3",
"claim": "Classical continuous bump-attractor models suffer from biologically unrealistic fine tuning of recurrent connectivity and from a stereotyped bump shape that cannot encode representational quality; a 2D Amari-type network with locally balanced E/I feedback removes both limitations.",
"cite_key": "Wojtak2024",
"evidence": "Two-dimensional Amari-type neural field model with locally balanced excitatory–inhibitory feedback loop, analyzed for stability and tested numerically against connectivity perturbations.",
"effect_size": "qualitative",
"text_access": "abstract_only",
"study_system": "2D continuous attractor neural-field network model",
"argument_role": "supporting",
"replication_status": "replication_unknown",
"claim_source_sentence": "Standard CAN models suffer from two major limitations: the stereotyped shape of the bump attractor does not reflect differences in the representational quality of WM items and the recurrent connections within the network require a biologically unrealistic level of fine tuning.",
"source_provenance_status": "non_substring_match",
"replication_evidence_dois": [],
"effect_size_source_sentence": null
}- source_refs
[
"paper:paper-68171c2580ca"
]
- source_span
Standard CAN models suffer from two major limitations: the stereotyped shape of the bump attractor does not reflect differences in the representational quality of WM items and the recurrent connections within the network require a biologically unrealistic level of fine tuning.
- evidence_refs
[
{
"ref": "paper:paper-68171c2580ca"
}
]- 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"
}