{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:4LMLHJ7ADV7SDA3JHW6RPNO2C4","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5e85ce8af3d45a861bc346cf94770314399c8409ea4fc8b028fdd2bb4c3a9b57","cross_cats_sorted":["math.PR","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2026-06-08T10:49:04Z","title_canon_sha256":"13599bfc0a03dda895d4fd3c238fc03a67e8d75d3b2672c45272686354878f3f"},"schema_version":"1.0","source":{"id":"2606.09328","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.09328","created_at":"2026-06-09T02:08:15Z"},{"alias_kind":"arxiv_version","alias_value":"2606.09328v1","created_at":"2026-06-09T02:08:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.09328","created_at":"2026-06-09T02:08:15Z"},{"alias_kind":"pith_short_12","alias_value":"4LMLHJ7ADV7S","created_at":"2026-06-09T02:08:15Z"},{"alias_kind":"pith_short_16","alias_value":"4LMLHJ7ADV7SDA3J","created_at":"2026-06-09T02:08:15Z"},{"alias_kind":"pith_short_8","alias_value":"4LMLHJ7A","created_at":"2026-06-09T02:08:15Z"}],"graph_snapshots":[{"event_id":"sha256:f6d1c7ee49388d4b11a564e57893e3e41c9ed897450ee89f8fb8a09b8db22d80","target":"graph","created_at":"2026-06-09T02:08:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.09328/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We investigate a generalized stochastic fractional neuronal model combining fractional dynamics with correlated stochastic inputs. The proposed framework is described by a fractional differential equation driven by a latent stochastic process with stationary increments and mean-reverting structure. This formulation allows the inclusion of both short-range and long-range dependence structures and naturally produces non-exponential relaxation phenomena. The main goal is the development of a feasible parameter estimation procedure based on discrete observations of the neuronal state process. We p","authors_text":"Enrica Pirozzi, Lauri Viitasaari, Luigia Caputo, Milla Laurikkala, Pauliina Ilmonen","cross_cats":["math.PR","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2026-06-08T10:49:04Z","title":"Parameter estimation in generalized fractional neuronal models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.09328","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:701bc42fe2b515c8ebc5f04c80d1ce84faf71fb0fdd1f8dba76074c4b8589a91","target":"record","created_at":"2026-06-09T02:08:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5e85ce8af3d45a861bc346cf94770314399c8409ea4fc8b028fdd2bb4c3a9b57","cross_cats_sorted":["math.PR","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2026-06-08T10:49:04Z","title_canon_sha256":"13599bfc0a03dda895d4fd3c238fc03a67e8d75d3b2672c45272686354878f3f"},"schema_version":"1.0","source":{"id":"2606.09328","kind":"arxiv","version":1}},"canonical_sha256":"e2d8b3a7e01d7f2183693dbd17b5da17026edd34dc23fdbe3b92ecbbddd65984","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e2d8b3a7e01d7f2183693dbd17b5da17026edd34dc23fdbe3b92ecbbddd65984","first_computed_at":"2026-06-09T02:08:15.462524Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T02:08:15.462524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Hl2qTrsRVPKEszDplrbn9MwQonFkgEqT75FPtgrrg8NYt1WrbHcLPoya0RFZXD/dEBHApnhimNimV5WxiWYkDg==","signature_status":"signed_v1","signed_at":"2026-06-09T02:08:15.463573Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.09328","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:701bc42fe2b515c8ebc5f04c80d1ce84faf71fb0fdd1f8dba76074c4b8589a91","sha256:f6d1c7ee49388d4b11a564e57893e3e41c9ed897450ee89f8fb8a09b8db22d80"],"state_sha256":"210bb069ba4aecdacee54f56741e738a690e7d64ffa78c5c004434bed0a1254b"}