{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:HDAKHCHKF4YA5WHSV4CGDHH6OZ","short_pith_number":"pith:HDAKHCHK","schema_version":"1.0","canonical_sha256":"38c0a388ea2f300ed8f2af04619cfe767ffbbd1450f5e65a6efd77764f43ef0e","source":{"kind":"arxiv","id":"1904.12966","version":1},"attestation_state":"computed","paper":{"title":"Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"H. Sebastian Seung, Jingpeng Wu, Kisuk Lee, Nicholas Turner, Ran Lu, Thomas Macrina","submitted_at":"2019-04-29T21:54:58Z","abstract_excerpt":"Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far. Convolutional nets were first applied to neuronal boundary detection a dozen years ago, and have now achieved impressive accuracy on clean images. Robust handling of image defects is a major outstanding challenge. Convolutional nets are also being employed for other tasks in neural circuit reconstruction: finding synapses and identifying synaptic partners, extending or prun"},"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":"1904.12966","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-29T21:54:58Z","cross_cats_sorted":[],"title_canon_sha256":"f7a45025db5e09f9e2651de1d8e421df165d1dc0de839524254e32ef9c6dfa0a","abstract_canon_sha256":"30133ca9459aaa2d55f4ef118f9f613fe2396f2d60d01135dc0fd3443571b121"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:24.333066Z","signature_b64":"+hC5rD4E3P/2mzzEOtNhWQSqUrTTEWoFRbj0Gn+lgkc6v7bc3RSzMzfglHb9dCYvZKqHAyOdp5G1ZfqthIzDCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38c0a388ea2f300ed8f2af04619cfe767ffbbd1450f5e65a6efd77764f43ef0e","last_reissued_at":"2026-05-17T23:47:24.332497Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:24.332497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"H. Sebastian Seung, Jingpeng Wu, Kisuk Lee, Nicholas Turner, Ran Lu, Thomas Macrina","submitted_at":"2019-04-29T21:54:58Z","abstract_excerpt":"Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far. Convolutional nets were first applied to neuronal boundary detection a dozen years ago, and have now achieved impressive accuracy on clean images. Robust handling of image defects is a major outstanding challenge. Convolutional nets are also being employed for other tasks in neural circuit reconstruction: finding synapses and identifying synaptic partners, extending or prun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.12966","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":"1904.12966","created_at":"2026-05-17T23:47:24.332574+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.12966v1","created_at":"2026-05-17T23:47:24.332574+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.12966","created_at":"2026-05-17T23:47:24.332574+00:00"},{"alias_kind":"pith_short_12","alias_value":"HDAKHCHKF4YA","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"HDAKHCHKF4YA5WHS","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"HDAKHCHK","created_at":"2026-05-18T12:33:18.533446+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/HDAKHCHKF4YA5WHSV4CGDHH6OZ","json":"https://pith.science/pith/HDAKHCHKF4YA5WHSV4CGDHH6OZ.json","graph_json":"https://pith.science/api/pith-number/HDAKHCHKF4YA5WHSV4CGDHH6OZ/graph.json","events_json":"https://pith.science/api/pith-number/HDAKHCHKF4YA5WHSV4CGDHH6OZ/events.json","paper":"https://pith.science/paper/HDAKHCHK"},"agent_actions":{"view_html":"https://pith.science/pith/HDAKHCHKF4YA5WHSV4CGDHH6OZ","download_json":"https://pith.science/pith/HDAKHCHKF4YA5WHSV4CGDHH6OZ.json","view_paper":"https://pith.science/paper/HDAKHCHK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.12966&json=true","fetch_graph":"https://pith.science/api/pith-number/HDAKHCHKF4YA5WHSV4CGDHH6OZ/graph.json","fetch_events":"https://pith.science/api/pith-number/HDAKHCHKF4YA5WHSV4CGDHH6OZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HDAKHCHKF4YA5WHSV4CGDHH6OZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HDAKHCHKF4YA5WHSV4CGDHH6OZ/action/storage_attestation","attest_author":"https://pith.science/pith/HDAKHCHKF4YA5WHSV4CGDHH6OZ/action/author_attestation","sign_citation":"https://pith.science/pith/HDAKHCHKF4YA5WHSV4CGDHH6OZ/action/citation_signature","submit_replication":"https://pith.science/pith/HDAKHCHKF4YA5WHSV4CGDHH6OZ/action/replication_record"}},"created_at":"2026-05-17T23:47:24.332574+00:00","updated_at":"2026-05-17T23:47:24.332574+00:00"}