{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:WUXMHHQ72K5Y5AXTHS7QXZJG4I","short_pith_number":"pith:WUXMHHQ7","schema_version":"1.0","canonical_sha256":"b52ec39e1fd2bb8e82f33cbf0be526e23167f0a4283b5eb3a097dd386c6d70f0","source":{"kind":"arxiv","id":"1906.09852","version":1},"attestation_state":"computed","paper":{"title":"Lifelong Learning Starting From Zero","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Claes Stranneg{\\aa}rd, Filip Slottner Seholm, Fredrik M\\\"akel\\\"ainen, Herman Carlstr\\\"om, Morteza Haghir Chehreghani, Niklas Engsner","submitted_at":"2019-06-24T11:18:00Z","abstract_excerpt":"We present a deep neural-network model for lifelong learning inspired by several forms of neuroplasticity. The neural network develops continuously in response to signals from the environment. In the beginning, the network is a blank slate with no nodes at all. It develops according to four rules: (i) expansion, which adds new nodes to memorize new input combinations; (ii) generalization, which adds new nodes that generalize from existing ones; (iii) forgetting, which removes nodes that are of relatively little use; and (iv) backpropagation, which fine-tunes the network parameters. We analyze "},"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":"1906.09852","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-24T11:18:00Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"727e288f94763817885c7021c5e6badcd74e83d133054c13e2ed3efda88c264d","abstract_canon_sha256":"ad86bb7feb37214c6cb9e27d0f0bf775bc91991386bdfd4fd781e844f8e49180"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:36.470102Z","signature_b64":"vdJIbqd2H/icqsBQU6I+5TjZz9Nn1LF7/uMwoyp0yYKzbApfjSfnlt76WAAF0YzDFpXSulh/FzppLK9plcYlAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b52ec39e1fd2bb8e82f33cbf0be526e23167f0a4283b5eb3a097dd386c6d70f0","last_reissued_at":"2026-05-17T23:42:36.469530Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:36.469530Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Lifelong Learning Starting From Zero","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Claes Stranneg{\\aa}rd, Filip Slottner Seholm, Fredrik M\\\"akel\\\"ainen, Herman Carlstr\\\"om, Morteza Haghir Chehreghani, Niklas Engsner","submitted_at":"2019-06-24T11:18:00Z","abstract_excerpt":"We present a deep neural-network model for lifelong learning inspired by several forms of neuroplasticity. The neural network develops continuously in response to signals from the environment. In the beginning, the network is a blank slate with no nodes at all. It develops according to four rules: (i) expansion, which adds new nodes to memorize new input combinations; (ii) generalization, which adds new nodes that generalize from existing ones; (iii) forgetting, which removes nodes that are of relatively little use; and (iv) backpropagation, which fine-tunes the network parameters. We analyze "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.09852","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":"1906.09852","created_at":"2026-05-17T23:42:36.469612+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.09852v1","created_at":"2026-05-17T23:42:36.469612+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.09852","created_at":"2026-05-17T23:42:36.469612+00:00"},{"alias_kind":"pith_short_12","alias_value":"WUXMHHQ72K5Y","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"WUXMHHQ72K5Y5AXT","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"WUXMHHQ7","created_at":"2026-05-18T12:33:33.725879+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/WUXMHHQ72K5Y5AXTHS7QXZJG4I","json":"https://pith.science/pith/WUXMHHQ72K5Y5AXTHS7QXZJG4I.json","graph_json":"https://pith.science/api/pith-number/WUXMHHQ72K5Y5AXTHS7QXZJG4I/graph.json","events_json":"https://pith.science/api/pith-number/WUXMHHQ72K5Y5AXTHS7QXZJG4I/events.json","paper":"https://pith.science/paper/WUXMHHQ7"},"agent_actions":{"view_html":"https://pith.science/pith/WUXMHHQ72K5Y5AXTHS7QXZJG4I","download_json":"https://pith.science/pith/WUXMHHQ72K5Y5AXTHS7QXZJG4I.json","view_paper":"https://pith.science/paper/WUXMHHQ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.09852&json=true","fetch_graph":"https://pith.science/api/pith-number/WUXMHHQ72K5Y5AXTHS7QXZJG4I/graph.json","fetch_events":"https://pith.science/api/pith-number/WUXMHHQ72K5Y5AXTHS7QXZJG4I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WUXMHHQ72K5Y5AXTHS7QXZJG4I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WUXMHHQ72K5Y5AXTHS7QXZJG4I/action/storage_attestation","attest_author":"https://pith.science/pith/WUXMHHQ72K5Y5AXTHS7QXZJG4I/action/author_attestation","sign_citation":"https://pith.science/pith/WUXMHHQ72K5Y5AXTHS7QXZJG4I/action/citation_signature","submit_replication":"https://pith.science/pith/WUXMHHQ72K5Y5AXTHS7QXZJG4I/action/replication_record"}},"created_at":"2026-05-17T23:42:36.469612+00:00","updated_at":"2026-05-17T23:42:36.469612+00:00"}