Version history

{
  "scope": "Theory (mapped to canonical cortical microcircuit)",
  "claim_text": "Recurrent Predictive Learning (RPL), a recurrent joint-embedding predictive architecture, computes prediction errors only in latent representation space (not input space) and learns sequence representations that resemble successor-like codes reported in human V1 and abstract sequence codes reported in macaque PFC — a circuit-centric theory framework that can be mapped onto canonical cortical microcircuits.",
  "raw_fields": {
    "n": 0,
    "doi": "10.1101/2025.11.25.690220",
    "claim": "Recurrent Predictive Learning (RPL), a recurrent joint-embedding predictive architecture, computes prediction errors only in latent representation space (not input space) and learns sequence representations that resemble successor-like codes reported in human V1 and abstract sequence codes reported in macaque PFC — a circuit-centric theory framework that can be mapped onto canonical cortical microcircuits.",
    "cite_key": "Mohammadi2025",
    "evidence": "JEPA-style theoretical model; representation comparisons with human V1 and macaque PFC; mapping to canonical microcircuit.",
    "effect_size": "model learns successor-like sequence representations matching human V1 and macaque PFC patterns",
    "text_access": "abstract_only",
    "study_system": "Theory (mapped to canonical cortical microcircuit)",
    "argument_role": "supporting",
    "replication_status": "theory",
    "claim_source_sentence": "Specifically, we introduce recurrent predictive learning (RPL), a recurrent joint-embedding predictive architecture inspired by self-supervised machine learning, that learns abstract representations of object identity and their dynamics and predicts future object motion. Crucially, the model learns sequence representations that resemble successor-like representations observed in the primary visual cortex of humans.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": "the model learns sequence representations that resemble successor-like representations observed in the primary visual cortex of humans."
  },
  "section_id": "section_16",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_16_evidence_package.json",
  "effect_size": "model learns successor-like sequence representations matching human V1 and macaque PFC patterns",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-16-synthesis",
  "source_kind": "review_finding",
  "source_path": "evidence/section_16_evidence_package.json",
  "source_refs": [
    "paper:paper-fbd1daca6501"
  ],
  "source_span": "Specifically, we introduce recurrent predictive learning (RPL), a recurrent joint-embedding predictive architecture inspired by self-supervised machine learning, that learns abstract representations of object identity and their dynamics and predicts future object motion. Crucially, the model learns sequence representations that resemble successor-like representations observed in the primary visual cortex of humans.",
  "study_system": "Theory (mapped to canonical cortical microcircuit)",
  "evidence_refs": [
    {
      "ref": "paper:paper-fbd1daca6501"
    }
  ],
  "section_title": "16. Synthesis — which computational claims the mouse-cortex E→E empirical record actually supports, where the bottleneck observations are, and what an inhibition-free, single-species, basic-research analytic framing misses",
  "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"
  },
  "evidence_summary": "JEPA-style theoretical model; representation comparisons with human V1 and macaque PFC; mapping to canonical microcircuit.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "theory",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-16-synthesis",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_16_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}