{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4NSW6DE24F5C636NTR264DXIP7","short_pith_number":"pith:4NSW6DE2","schema_version":"1.0","canonical_sha256":"e3656f0c9ae17a2f6fcd9c75ee0ee87fcd8c07c3cae3aa384aaf43a5a668bed1","source":{"kind":"arxiv","id":"2509.02799","version":2},"attestation_state":"computed","paper":{"title":"Data-driven mean-field within whole-brain models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.NC","authors_text":"Marmaduke Woodman, Martin Breyton, Meysam Hashemi, Spase Petkoski, Viktor Jirsa, Viktor Sip","submitted_at":"2025-09-02T20:02:00Z","abstract_excerpt":"Mean-field models provide a link between microscopic neuronal activity and macroscopic brain dynamics. Their derivation depends on simplifying assumptions, such as all-to-all connectivity, limiting their biological realism. To overcome this, we introduce a data-driven framework in which a multi-layer perceptron (MLP) learns the macroscopic dynamics directly from simulations of a network of spiking neurons. The network connection probability serves here as a new parameter, inaccessible to purely analytical treatment, which is validated against ground truth analytical solutions. Through bifurcat"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2509.02799","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"q-bio.NC","submitted_at":"2025-09-02T20:02:00Z","cross_cats_sorted":[],"title_canon_sha256":"778b389dee1568d1f4323d21d50e8cb249e7a1013bc112cbeac3e7358f0e1fff","abstract_canon_sha256":"c45596c86dbc411643d5de7e8a279536af5caf73375680f9ece4df45f84b8912"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:04:41.279815Z","signature_b64":"ZzsVKnGa9dDnTTsJgf72mFRSNdP8Lcqr3m0KUqJtv5m6I8EkFucizHIyRZNIGkYO3Jv4T71GTNdVas8ScbnyCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e3656f0c9ae17a2f6fcd9c75ee0ee87fcd8c07c3cae3aa384aaf43a5a668bed1","last_reissued_at":"2026-07-05T12:04:41.279388Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:04:41.279388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data-driven mean-field within whole-brain models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.NC","authors_text":"Marmaduke Woodman, Martin Breyton, Meysam Hashemi, Spase Petkoski, Viktor Jirsa, Viktor Sip","submitted_at":"2025-09-02T20:02:00Z","abstract_excerpt":"Mean-field models provide a link between microscopic neuronal activity and macroscopic brain dynamics. Their derivation depends on simplifying assumptions, such as all-to-all connectivity, limiting their biological realism. To overcome this, we introduce a data-driven framework in which a multi-layer perceptron (MLP) learns the macroscopic dynamics directly from simulations of a network of spiking neurons. The network connection probability serves here as a new parameter, inaccessible to purely analytical treatment, which is validated against ground truth analytical solutions. Through bifurcat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.02799","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.02799/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2509.02799","created_at":"2026-07-05T12:04:41.279450+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.02799v2","created_at":"2026-07-05T12:04:41.279450+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.02799","created_at":"2026-07-05T12:04:41.279450+00:00"},{"alias_kind":"pith_short_12","alias_value":"4NSW6DE24F5C","created_at":"2026-07-05T12:04:41.279450+00:00"},{"alias_kind":"pith_short_16","alias_value":"4NSW6DE24F5C636N","created_at":"2026-07-05T12:04:41.279450+00:00"},{"alias_kind":"pith_short_8","alias_value":"4NSW6DE2","created_at":"2026-07-05T12:04:41.279450+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.00306","citing_title":"Mechanistic bridges from receptors to whole-brain dynamics: mean-field reductions, validity domains, and computational trade-offs","ref_index":42,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4NSW6DE24F5C636NTR264DXIP7","json":"https://pith.science/pith/4NSW6DE24F5C636NTR264DXIP7.json","graph_json":"https://pith.science/api/pith-number/4NSW6DE24F5C636NTR264DXIP7/graph.json","events_json":"https://pith.science/api/pith-number/4NSW6DE24F5C636NTR264DXIP7/events.json","paper":"https://pith.science/paper/4NSW6DE2"},"agent_actions":{"view_html":"https://pith.science/pith/4NSW6DE24F5C636NTR264DXIP7","download_json":"https://pith.science/pith/4NSW6DE24F5C636NTR264DXIP7.json","view_paper":"https://pith.science/paper/4NSW6DE2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.02799&json=true","fetch_graph":"https://pith.science/api/pith-number/4NSW6DE24F5C636NTR264DXIP7/graph.json","fetch_events":"https://pith.science/api/pith-number/4NSW6DE24F5C636NTR264DXIP7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4NSW6DE24F5C636NTR264DXIP7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4NSW6DE24F5C636NTR264DXIP7/action/storage_attestation","attest_author":"https://pith.science/pith/4NSW6DE24F5C636NTR264DXIP7/action/author_attestation","sign_citation":"https://pith.science/pith/4NSW6DE24F5C636NTR264DXIP7/action/citation_signature","submit_replication":"https://pith.science/pith/4NSW6DE24F5C636NTR264DXIP7/action/replication_record"}},"created_at":"2026-07-05T12:04:41.279450+00:00","updated_at":"2026-07-05T12:04:41.279450+00:00"}