{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:S7TPNFAN4NRGA5WIMILYJFOXBE","short_pith_number":"pith:S7TPNFAN","canonical_record":{"source":{"id":"1805.01352","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-04-28T06:44:13Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"fdd636c9fc17737760be2001e2d5a3cf9e7ed614b2810847ac69c2d7c35cb077","abstract_canon_sha256":"2e633c24c7f25729d9745f53893c26386b026c3cb3d1f0da2b0b51e33d4c86ce"},"schema_version":"1.0"},"canonical_sha256":"97e6f6940de3626076c862178495d7090bd6161149ca1be32c8a344514689a74","source":{"kind":"arxiv","id":"1805.01352","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.01352","created_at":"2026-07-05T01:07:35Z"},{"alias_kind":"arxiv_version","alias_value":"1805.01352v2","created_at":"2026-07-05T01:07:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.01352","created_at":"2026-07-05T01:07:35Z"},{"alias_kind":"pith_short_12","alias_value":"S7TPNFAN4NRG","created_at":"2026-07-05T01:07:35Z"},{"alias_kind":"pith_short_16","alias_value":"S7TPNFAN4NRGA5WI","created_at":"2026-07-05T01:07:35Z"},{"alias_kind":"pith_short_8","alias_value":"S7TPNFAN","created_at":"2026-07-05T01:07:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:S7TPNFAN4NRGA5WIMILYJFOXBE","target":"record","payload":{"canonical_record":{"source":{"id":"1805.01352","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-04-28T06:44:13Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"fdd636c9fc17737760be2001e2d5a3cf9e7ed614b2810847ac69c2d7c35cb077","abstract_canon_sha256":"2e633c24c7f25729d9745f53893c26386b026c3cb3d1f0da2b0b51e33d4c86ce"},"schema_version":"1.0"},"canonical_sha256":"97e6f6940de3626076c862178495d7090bd6161149ca1be32c8a344514689a74","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:07:35.845280Z","signature_b64":"u4eFLoN6H41p4BV/awze8bZnF1eXmARStmLszxoDRfIq3sekr+zV1dtG/klyvm8PRlYLPQPxtb3CnIWCFBp9AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"97e6f6940de3626076c862178495d7090bd6161149ca1be32c8a344514689a74","last_reissued_at":"2026-07-05T01:07:35.844841Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:07:35.844841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.01352","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:07:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jf2F16uKiswW4bE+bC8pU5RK/Y+7Ky/waSg1+8nUxXrViFfMF/iSsWQyVHpTCcQsDTC2eTOotFrJu1vTspEnCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T16:30:02.140422Z"},"content_sha256":"3c79cf241b8b1d3391299466af1a978132c910432deaebea7facc041fd4b6d07","schema_version":"1.0","event_id":"sha256:3c79cf241b8b1d3391299466af1a978132c910432deaebea7facc041fd4b6d07"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:S7TPNFAN4NRGA5WIMILYJFOXBE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spiking Deep Residual Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.NE","authors_text":"Gang Pan, Huajin Tang, Yangfan Hu","submitted_at":"2018-04-28T06:44:13Z","abstract_excerpt":"Spiking neural networks (SNNs) have received significant attention for their biological plausibility. SNNs theoretically have at least the same computational power as traditional artificial neural networks (ANNs). They possess potential of achieving energy-efficiency while keeping comparable performance to deep neural networks (DNNs). However, it is still a big challenge to train a very deep SNN. In this paper, we propose an efficient approach to build a spiking version of deep residual network (ResNet). ResNet is considered as a kind of the state-of-the-art convolutional neural networks (CNNs"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.01352","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/1805.01352/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:07:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rs1nFyT8krcBlmDRODEmWiUXhRMWZ97izzxO+yhw3+62EGrPJCQjIhXPtlXZCsd18uf6l3MpvFdC1DX4fciSCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T16:30:02.140973Z"},"content_sha256":"ca9897e3382df5881c2b8edd9df9b022b63763d76c1a94a21f7d035431232a09","schema_version":"1.0","event_id":"sha256:ca9897e3382df5881c2b8edd9df9b022b63763d76c1a94a21f7d035431232a09"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S7TPNFAN4NRGA5WIMILYJFOXBE/bundle.json","state_url":"https://pith.science/pith/S7TPNFAN4NRGA5WIMILYJFOXBE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S7TPNFAN4NRGA5WIMILYJFOXBE/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T16:30:02Z","links":{"resolver":"https://pith.science/pith/S7TPNFAN4NRGA5WIMILYJFOXBE","bundle":"https://pith.science/pith/S7TPNFAN4NRGA5WIMILYJFOXBE/bundle.json","state":"https://pith.science/pith/S7TPNFAN4NRGA5WIMILYJFOXBE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S7TPNFAN4NRGA5WIMILYJFOXBE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:S7TPNFAN4NRGA5WIMILYJFOXBE","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":"2e633c24c7f25729d9745f53893c26386b026c3cb3d1f0da2b0b51e33d4c86ce","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-04-28T06:44:13Z","title_canon_sha256":"fdd636c9fc17737760be2001e2d5a3cf9e7ed614b2810847ac69c2d7c35cb077"},"schema_version":"1.0","source":{"id":"1805.01352","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.01352","created_at":"2026-07-05T01:07:35Z"},{"alias_kind":"arxiv_version","alias_value":"1805.01352v2","created_at":"2026-07-05T01:07:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.01352","created_at":"2026-07-05T01:07:35Z"},{"alias_kind":"pith_short_12","alias_value":"S7TPNFAN4NRG","created_at":"2026-07-05T01:07:35Z"},{"alias_kind":"pith_short_16","alias_value":"S7TPNFAN4NRGA5WI","created_at":"2026-07-05T01:07:35Z"},{"alias_kind":"pith_short_8","alias_value":"S7TPNFAN","created_at":"2026-07-05T01:07:35Z"}],"graph_snapshots":[{"event_id":"sha256:ca9897e3382df5881c2b8edd9df9b022b63763d76c1a94a21f7d035431232a09","target":"graph","created_at":"2026-07-05T01:07:35Z","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/1805.01352/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Spiking neural networks (SNNs) have received significant attention for their biological plausibility. SNNs theoretically have at least the same computational power as traditional artificial neural networks (ANNs). They possess potential of achieving energy-efficiency while keeping comparable performance to deep neural networks (DNNs). However, it is still a big challenge to train a very deep SNN. In this paper, we propose an efficient approach to build a spiking version of deep residual network (ResNet). ResNet is considered as a kind of the state-of-the-art convolutional neural networks (CNNs","authors_text":"Gang Pan, Huajin Tang, Yangfan Hu","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-04-28T06:44:13Z","title":"Spiking Deep Residual Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.01352","kind":"arxiv","version":2},"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":"sha256:3c79cf241b8b1d3391299466af1a978132c910432deaebea7facc041fd4b6d07","target":"record","created_at":"2026-07-05T01:07:35Z","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":"2e633c24c7f25729d9745f53893c26386b026c3cb3d1f0da2b0b51e33d4c86ce","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-04-28T06:44:13Z","title_canon_sha256":"fdd636c9fc17737760be2001e2d5a3cf9e7ed614b2810847ac69c2d7c35cb077"},"schema_version":"1.0","source":{"id":"1805.01352","kind":"arxiv","version":2}},"canonical_sha256":"97e6f6940de3626076c862178495d7090bd6161149ca1be32c8a344514689a74","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"97e6f6940de3626076c862178495d7090bd6161149ca1be32c8a344514689a74","first_computed_at":"2026-07-05T01:07:35.844841Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:07:35.844841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u4eFLoN6H41p4BV/awze8bZnF1eXmARStmLszxoDRfIq3sekr+zV1dtG/klyvm8PRlYLPQPxtb3CnIWCFBp9AA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:07:35.845280Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.01352","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3c79cf241b8b1d3391299466af1a978132c910432deaebea7facc041fd4b6d07","sha256:ca9897e3382df5881c2b8edd9df9b022b63763d76c1a94a21f7d035431232a09"],"state_sha256":"17f4d1deae15f11e226d97f6ce44b5e047ae193cad31f494f6e8cd600cf24aeb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4sSOsIjvnx9WTTWLuun57n1L0tclrBixBkp9WxsSYN8RdDB7SlYHPpMrB8LaL58Q0h9BKue/2iiZ6U/LMrBrDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T16:30:02.147875Z","bundle_sha256":"c9f60209e61b149309a662a8d35029cdf3e6911649b7e48434c49c50a2e2227a"}}