Version history

{
  "scope": "Modular LSM (modelling); MNIST benchmark",
  "claim_text": "A modular liquid state machine with biologically-motivated input synapses outperforms small-world / random LSMs on MNIST and is more robust to damage — relevant to reservoir-computing accounts of cortex.",
  "raw_fields": {
    "n": null,
    "doi": "10.3389/fncom.2021.594337",
    "claim": "A modular liquid state machine with biologically-motivated input synapses outperforms small-world / random LSMs on MNIST and is more robust to damage — relevant to reservoir-computing accounts of cortex.",
    "cite_key": "Dai2021",
    "evidence": "Bio-inspired modular LSM benchmarked on MNIST with Poisson encoding; robustness compared with small-world and random topologies.",
    "effect_size": "Higher MNIST accuracy and better robustness to damage than small-world / random networks",
    "text_access": "abstract_only",
    "study_system": "Modular LSM (modelling); MNIST benchmark",
    "argument_role": "supporting",
    "replication_status": "replication_unknown",
    "claim_source_sentence": "We show that the proposed structure can not only significantly reduce the computational complexity but also achieve higher performance compared to the structure of previous reported networks of a similar size.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": "We also show that the proposed structure has better robustness against system damage than the small-world and random structures."
  },
  "section_id": "section_14",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_14_evidence_package.json",
  "effect_size": "Higher MNIST accuracy and better robustness to damage than small-world / random networks",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-14-predictive-coding",
  "source_kind": "review_finding",
  "source_path": "evidence/section_14_evidence_package.json",
  "source_refs": [
    "paper:paper-61f14ffd1cf7"
  ],
  "source_span": "We show that the proposed structure can not only significantly reduce the computational complexity but also achieve higher performance compared to the structure of previous reported networks of a similar size.",
  "study_system": "Modular LSM (modelling); MNIST benchmark",
  "evidence_refs": [
    {
      "ref": "paper:paper-61f14ffd1cf7"
    }
  ],
  "section_title": "14. Predictive-coding and dynamical-systems accounts — the role of recurrent excitatory feedback in error signalling, state estimation, and reservoir computing, evaluated against mouse data",
  "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": "Bio-inspired modular LSM benchmarked on MNIST with Poisson encoding; robustness compared with small-world and random topologies.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "replication_unknown",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-14-predictive-coding",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_14_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}