{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:WEOJCTLTEV6JQOQBJFFJE3XRPA","short_pith_number":"pith:WEOJCTLT","canonical_record":{"source":{"id":"1807.10478","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-27T08:02:45Z","cross_cats_sorted":["math.DS","stat.ML"],"title_canon_sha256":"715f47c36e3a449bcf1fdc9681faa43dc85bc5e61e9dc835853486bc3a2cd867","abstract_canon_sha256":"fa698cc42fba7c73281758f3998c9a0551fce9752dbc6d57e4ef947b3f8d5c89"},"schema_version":"1.0"},"canonical_sha256":"b11c914d73257c983a01494a926ef1782d9a017c952bc52fd48ead7fc8c08e76","source":{"kind":"arxiv","id":"1807.10478","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.10478","created_at":"2026-05-17T23:51:43Z"},{"alias_kind":"arxiv_version","alias_value":"1807.10478v6","created_at":"2026-05-17T23:51:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.10478","created_at":"2026-05-17T23:51:43Z"},{"alias_kind":"pith_short_12","alias_value":"WEOJCTLTEV6J","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"WEOJCTLTEV6JQOQB","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"WEOJCTLT","created_at":"2026-05-18T12:32:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:WEOJCTLTEV6JQOQBJFFJE3XRPA","target":"record","payload":{"canonical_record":{"source":{"id":"1807.10478","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-27T08:02:45Z","cross_cats_sorted":["math.DS","stat.ML"],"title_canon_sha256":"715f47c36e3a449bcf1fdc9681faa43dc85bc5e61e9dc835853486bc3a2cd867","abstract_canon_sha256":"fa698cc42fba7c73281758f3998c9a0551fce9752dbc6d57e4ef947b3f8d5c89"},"schema_version":"1.0"},"canonical_sha256":"b11c914d73257c983a01494a926ef1782d9a017c952bc52fd48ead7fc8c08e76","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:43.995816Z","signature_b64":"ziKml9i8eDuV+hYz1HDsvzqT42zPISWELFYtLQYeWOx5fxFBl+wdVR5pgnIFt08JJS3SQus8TnozJoXnObllDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b11c914d73257c983a01494a926ef1782d9a017c952bc52fd48ead7fc8c08e76","last_reissued_at":"2026-05-17T23:51:43.995341Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:43.995341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.10478","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:51:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LDqHN391HeoeH2/rw0lf01FPBUtqGoMDv6fvHNPryk7HVCyLgVoTxHi3FG6kd9Sj7RrHQSRA7Ztyqc4k69lfCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T20:38:53.540881Z"},"content_sha256":"44b781504a6c049adbb360cde5228cb3d33ed360c32d6aa43efb076548010172","schema_version":"1.0","event_id":"sha256:44b781504a6c049adbb360cde5228cb3d33ed360c32d6aa43efb076548010172"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:WEOJCTLTEV6JQOQBJFFJE3XRPA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Interpreting recurrent neural networks behaviour via excitable network attractors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.DS","stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrea Ceni, Lorenzo Livi, Peter Ashwin","submitted_at":"2018-07-27T08:02:45Z","abstract_excerpt":"Introduction: Machine learning provides fundamental tools both for scientific research and for the development of technologies with significant impact on society. It provides methods that facilitate the discovery of regularities in data and that give predictions without explicit knowledge of the rules governing a system. However, a price is paid for exploiting such flexibility: machine learning methods are typically black-boxes where it is difficult to fully understand what the machine is doing or how it is operating. This poses constraints on the applicability and explainability of such metho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.10478","kind":"arxiv","version":6},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:51:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NoG/uYaNgFNhCn9UpSx4vO3SY6gmoCX48qqRpx/4yGSV4U6/La8vbBt2JcctppY0OD7VlYTRoGh5aqS9XPzKDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T20:38:53.541831Z"},"content_sha256":"b6c7f26b819fdfe027db59a2b6e4c753bb5e0ccfeb8835004205649a5c42352a","schema_version":"1.0","event_id":"sha256:b6c7f26b819fdfe027db59a2b6e4c753bb5e0ccfeb8835004205649a5c42352a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WEOJCTLTEV6JQOQBJFFJE3XRPA/bundle.json","state_url":"https://pith.science/pith/WEOJCTLTEV6JQOQBJFFJE3XRPA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WEOJCTLTEV6JQOQBJFFJE3XRPA/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":"2026-06-05T20:38:53Z","links":{"resolver":"https://pith.science/pith/WEOJCTLTEV6JQOQBJFFJE3XRPA","bundle":"https://pith.science/pith/WEOJCTLTEV6JQOQBJFFJE3XRPA/bundle.json","state":"https://pith.science/pith/WEOJCTLTEV6JQOQBJFFJE3XRPA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WEOJCTLTEV6JQOQBJFFJE3XRPA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WEOJCTLTEV6JQOQBJFFJE3XRPA","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":"fa698cc42fba7c73281758f3998c9a0551fce9752dbc6d57e4ef947b3f8d5c89","cross_cats_sorted":["math.DS","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-27T08:02:45Z","title_canon_sha256":"715f47c36e3a449bcf1fdc9681faa43dc85bc5e61e9dc835853486bc3a2cd867"},"schema_version":"1.0","source":{"id":"1807.10478","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.10478","created_at":"2026-05-17T23:51:43Z"},{"alias_kind":"arxiv_version","alias_value":"1807.10478v6","created_at":"2026-05-17T23:51:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.10478","created_at":"2026-05-17T23:51:43Z"},{"alias_kind":"pith_short_12","alias_value":"WEOJCTLTEV6J","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"WEOJCTLTEV6JQOQB","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"WEOJCTLT","created_at":"2026-05-18T12:32:59Z"}],"graph_snapshots":[{"event_id":"sha256:b6c7f26b819fdfe027db59a2b6e4c753bb5e0ccfeb8835004205649a5c42352a","target":"graph","created_at":"2026-05-17T23:51:43Z","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"},"paper":{"abstract_excerpt":"Introduction: Machine learning provides fundamental tools both for scientific research and for the development of technologies with significant impact on society. It provides methods that facilitate the discovery of regularities in data and that give predictions without explicit knowledge of the rules governing a system. However, a price is paid for exploiting such flexibility: machine learning methods are typically black-boxes where it is difficult to fully understand what the machine is doing or how it is operating. This poses constraints on the applicability and explainability of such metho","authors_text":"Andrea Ceni, Lorenzo Livi, Peter Ashwin","cross_cats":["math.DS","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-27T08:02:45Z","title":"Interpreting recurrent neural networks behaviour via excitable network attractors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.10478","kind":"arxiv","version":6},"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":"sha256:44b781504a6c049adbb360cde5228cb3d33ed360c32d6aa43efb076548010172","target":"record","created_at":"2026-05-17T23:51:43Z","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":"fa698cc42fba7c73281758f3998c9a0551fce9752dbc6d57e4ef947b3f8d5c89","cross_cats_sorted":["math.DS","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-27T08:02:45Z","title_canon_sha256":"715f47c36e3a449bcf1fdc9681faa43dc85bc5e61e9dc835853486bc3a2cd867"},"schema_version":"1.0","source":{"id":"1807.10478","kind":"arxiv","version":6}},"canonical_sha256":"b11c914d73257c983a01494a926ef1782d9a017c952bc52fd48ead7fc8c08e76","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b11c914d73257c983a01494a926ef1782d9a017c952bc52fd48ead7fc8c08e76","first_computed_at":"2026-05-17T23:51:43.995341Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:51:43.995341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ziKml9i8eDuV+hYz1HDsvzqT42zPISWELFYtLQYeWOx5fxFBl+wdVR5pgnIFt08JJS3SQus8TnozJoXnObllDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:51:43.995816Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.10478","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:44b781504a6c049adbb360cde5228cb3d33ed360c32d6aa43efb076548010172","sha256:b6c7f26b819fdfe027db59a2b6e4c753bb5e0ccfeb8835004205649a5c42352a"],"state_sha256":"56b00a64767885a18518d6bf6a83fa3431cb57fa98eba17a643e61d789c98d89"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k2q+twn22sOMUUgveiFUOynXuNEPhObzwdkteD6Fsnog3jeJi2tw4MQLkM+o5bdGNq206LNiAhab6ebvx/alDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T20:38:53.545518Z","bundle_sha256":"c3938a23afef3972a5e69ffdd5e850ea53f244f9cb8781cb87fe0b489841f6dc"}}