{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:6HNIJL5S37KIOH5BVL45DZPMTF","short_pith_number":"pith:6HNIJL5S","schema_version":"1.0","canonical_sha256":"f1da84afb2dfd4871fa1aaf9d1e5ec9942605c4e2688aaa092ba23a689a701ec","source":{"kind":"arxiv","id":"1702.05394","version":2},"attestation_state":"computed","paper":{"title":"Measures of spike train synchrony for data with multiple time-scales","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.NC"],"primary_cat":"physics.data-an","authors_text":"Eero Satuvuori, Fleur Zeldenrust, Irene Malvestio, Kerstin Lenk, Mario Mulansky, Nebojsa Bozanic, Thomas Kreuz","submitted_at":"2017-02-17T15:37:46Z","abstract_excerpt":"Background: Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time-scale parametric measures, since by adapting to the local firing rate they take into account all the time-scales of a given dataset.\n  New Method: In data containing multiple time-scales (e.g. regular spiking and bursts) one is typically less interested in the smallest time-scales and a more adaptive approach is needed. Here we propose "},"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":"1702.05394","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2017-02-17T15:37:46Z","cross_cats_sorted":["q-bio.NC"],"title_canon_sha256":"dd901e98a79abf5af49ac1ac8ced1aca84cef93c3bf2057ce789d1e058a41bb9","abstract_canon_sha256":"62bc05bedaf2c268943be0c513400481cf6c3384f747a2adb15f25a46614ba2d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:26.659733Z","signature_b64":"D+lzwDS/moPW5H5wOz6uEPIvnFDCwsmqLQrjQqOkbvcGiDgeZVDLVy7yRStM59bC8KcDN0Sj5BOs9POAJ+BnBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f1da84afb2dfd4871fa1aaf9d1e5ec9942605c4e2688aaa092ba23a689a701ec","last_reissued_at":"2026-05-18T00:43:26.659086Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:26.659086Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Measures of spike train synchrony for data with multiple time-scales","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.NC"],"primary_cat":"physics.data-an","authors_text":"Eero Satuvuori, Fleur Zeldenrust, Irene Malvestio, Kerstin Lenk, Mario Mulansky, Nebojsa Bozanic, Thomas Kreuz","submitted_at":"2017-02-17T15:37:46Z","abstract_excerpt":"Background: Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time-scale parametric measures, since by adapting to the local firing rate they take into account all the time-scales of a given dataset.\n  New Method: In data containing multiple time-scales (e.g. regular spiking and bursts) one is typically less interested in the smallest time-scales and a more adaptive approach is needed. Here we propose "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.05394","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":""},"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":"1702.05394","created_at":"2026-05-18T00:43:26.659168+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.05394v2","created_at":"2026-05-18T00:43:26.659168+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.05394","created_at":"2026-05-18T00:43:26.659168+00:00"},{"alias_kind":"pith_short_12","alias_value":"6HNIJL5S37KI","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"6HNIJL5S37KIOH5B","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"6HNIJL5S","created_at":"2026-05-18T12:31:03.183658+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6HNIJL5S37KIOH5BVL45DZPMTF","json":"https://pith.science/pith/6HNIJL5S37KIOH5BVL45DZPMTF.json","graph_json":"https://pith.science/api/pith-number/6HNIJL5S37KIOH5BVL45DZPMTF/graph.json","events_json":"https://pith.science/api/pith-number/6HNIJL5S37KIOH5BVL45DZPMTF/events.json","paper":"https://pith.science/paper/6HNIJL5S"},"agent_actions":{"view_html":"https://pith.science/pith/6HNIJL5S37KIOH5BVL45DZPMTF","download_json":"https://pith.science/pith/6HNIJL5S37KIOH5BVL45DZPMTF.json","view_paper":"https://pith.science/paper/6HNIJL5S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.05394&json=true","fetch_graph":"https://pith.science/api/pith-number/6HNIJL5S37KIOH5BVL45DZPMTF/graph.json","fetch_events":"https://pith.science/api/pith-number/6HNIJL5S37KIOH5BVL45DZPMTF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6HNIJL5S37KIOH5BVL45DZPMTF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6HNIJL5S37KIOH5BVL45DZPMTF/action/storage_attestation","attest_author":"https://pith.science/pith/6HNIJL5S37KIOH5BVL45DZPMTF/action/author_attestation","sign_citation":"https://pith.science/pith/6HNIJL5S37KIOH5BVL45DZPMTF/action/citation_signature","submit_replication":"https://pith.science/pith/6HNIJL5S37KIOH5BVL45DZPMTF/action/replication_record"}},"created_at":"2026-05-18T00:43:26.659168+00:00","updated_at":"2026-05-18T00:43:26.659168+00:00"}