{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:O3KO26ZXKK2P4HV2TCPMNNP6PV","short_pith_number":"pith:O3KO26ZX","schema_version":"1.0","canonical_sha256":"76d4ed7b3752b4fe1eba989ec6b5fe7d50aebf6ff2e083cf216e7a232f299da3","source":{"kind":"arxiv","id":"1503.00135","version":1},"attestation_state":"computed","paper":{"title":"Supervised learning sets benchmark for robust spike detection from calcium imaging signals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ML","authors_text":"Andreas Tolias, Emmanouil Froudarakis, Jacob Reimer, Lucas Theis, Matthias Bethge, Miroslav Rom\\'an Ros\\'on, Philipp Berens, Thomas Euler, Tom Baden","submitted_at":"2015-02-28T14:52:33Z","abstract_excerpt":"A fundamental challenge in calcium imaging has been to infer the timing of action potentials from the measured noisy calcium fluorescence traces. We systematically evaluate a range of spike inference algorithms on a large benchmark dataset recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCamp6). We show that a new algorithm based on supervised learning in flexible probabilistic models outperforms all previously published techniques, setting a new standard for spike inference from calcium signals. Importantly, it performs better than other algor"},"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":"1503.00135","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-02-28T14:52:33Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"f1c74ed4fe7171c190b6d3c68edf8cb6ffbb0cb5ed868a580be4d8106c6092ed","abstract_canon_sha256":"ba2aa068a23ff52682009c062647a15bc83d495293371638ea575355c8c0a585"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:25:55.078986Z","signature_b64":"93pF7EtY9C64o3Alb7C+l393QKooIyqy5nzfJT8LWuI6JmkgytTVpxRQTxUkDYNTWtBDJdn9+4rdUv8D/DfzCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"76d4ed7b3752b4fe1eba989ec6b5fe7d50aebf6ff2e083cf216e7a232f299da3","last_reissued_at":"2026-05-18T02:25:55.078631Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:25:55.078631Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Supervised learning sets benchmark for robust spike detection from calcium imaging signals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ML","authors_text":"Andreas Tolias, Emmanouil Froudarakis, Jacob Reimer, Lucas Theis, Matthias Bethge, Miroslav Rom\\'an Ros\\'on, Philipp Berens, Thomas Euler, Tom Baden","submitted_at":"2015-02-28T14:52:33Z","abstract_excerpt":"A fundamental challenge in calcium imaging has been to infer the timing of action potentials from the measured noisy calcium fluorescence traces. We systematically evaluate a range of spike inference algorithms on a large benchmark dataset recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCamp6). We show that a new algorithm based on supervised learning in flexible probabilistic models outperforms all previously published techniques, setting a new standard for spike inference from calcium signals. Importantly, it performs better than other algor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.00135","kind":"arxiv","version":1},"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":"1503.00135","created_at":"2026-05-18T02:25:55.078693+00:00"},{"alias_kind":"arxiv_version","alias_value":"1503.00135v1","created_at":"2026-05-18T02:25:55.078693+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.00135","created_at":"2026-05-18T02:25:55.078693+00:00"},{"alias_kind":"pith_short_12","alias_value":"O3KO26ZXKK2P","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_16","alias_value":"O3KO26ZXKK2P4HV2","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_8","alias_value":"O3KO26ZX","created_at":"2026-05-18T12:29:34.919912+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/O3KO26ZXKK2P4HV2TCPMNNP6PV","json":"https://pith.science/pith/O3KO26ZXKK2P4HV2TCPMNNP6PV.json","graph_json":"https://pith.science/api/pith-number/O3KO26ZXKK2P4HV2TCPMNNP6PV/graph.json","events_json":"https://pith.science/api/pith-number/O3KO26ZXKK2P4HV2TCPMNNP6PV/events.json","paper":"https://pith.science/paper/O3KO26ZX"},"agent_actions":{"view_html":"https://pith.science/pith/O3KO26ZXKK2P4HV2TCPMNNP6PV","download_json":"https://pith.science/pith/O3KO26ZXKK2P4HV2TCPMNNP6PV.json","view_paper":"https://pith.science/paper/O3KO26ZX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1503.00135&json=true","fetch_graph":"https://pith.science/api/pith-number/O3KO26ZXKK2P4HV2TCPMNNP6PV/graph.json","fetch_events":"https://pith.science/api/pith-number/O3KO26ZXKK2P4HV2TCPMNNP6PV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O3KO26ZXKK2P4HV2TCPMNNP6PV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O3KO26ZXKK2P4HV2TCPMNNP6PV/action/storage_attestation","attest_author":"https://pith.science/pith/O3KO26ZXKK2P4HV2TCPMNNP6PV/action/author_attestation","sign_citation":"https://pith.science/pith/O3KO26ZXKK2P4HV2TCPMNNP6PV/action/citation_signature","submit_replication":"https://pith.science/pith/O3KO26ZXKK2P4HV2TCPMNNP6PV/action/replication_record"}},"created_at":"2026-05-18T02:25:55.078693+00:00","updated_at":"2026-05-18T02:25:55.078693+00:00"}