{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:3TKR2CZ7XYODRVCGGXMU3ZYBGI","short_pith_number":"pith:3TKR2CZ7","schema_version":"1.0","canonical_sha256":"dcd51d0b3fbe1c38d44635d94de701320b8ee16abe50ca1f1961b5c779d3e29f","source":{"kind":"arxiv","id":"1901.09049","version":2},"attestation_state":"computed","paper":{"title":"Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Darjan Salaj, Elias Hajek, Franz Scherr, Guillaume Bellec, Robert Legenstein, Wolfgang Maass","submitted_at":"2019-01-25T19:07:36Z","abstract_excerpt":"The way how recurrently connected networks of spiking neurons in the brain acquire powerful information processing capabilities through learning has remained a mystery. This lack of understanding is linked to a lack of learning algorithms for recurrent networks of spiking neurons (RSNNs) that are both functionally powerful and can be implemented by known biological mechanisms. Since RSNNs are simultaneously a primary target for implementations of brain-inspired circuits in neuromorphic hardware, this lack of algorithmic insight also hinders technological progress in that area. The gold standar"},"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":"1901.09049","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-01-25T19:07:36Z","cross_cats_sorted":[],"title_canon_sha256":"bc8fe54a1429151680680ed020e0735db0306e78da2becf92c3ab5ffa69bcd08","abstract_canon_sha256":"1f4965ba3dd2ec3c8661d67820284f29074ac3e64414782695343bcf29264a54"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:53:02.498429Z","signature_b64":"KOVPcd52frJFYuPAMjcXCXHXnEtdZqlr9BgQ0dEhi21l1nBeHnxe9iGxNPcuy11pRfGYXSdtYuXkjNB+HBrsCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dcd51d0b3fbe1c38d44635d94de701320b8ee16abe50ca1f1961b5c779d3e29f","last_reissued_at":"2026-05-17T23:53:02.497906Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:53:02.497906Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Darjan Salaj, Elias Hajek, Franz Scherr, Guillaume Bellec, Robert Legenstein, Wolfgang Maass","submitted_at":"2019-01-25T19:07:36Z","abstract_excerpt":"The way how recurrently connected networks of spiking neurons in the brain acquire powerful information processing capabilities through learning has remained a mystery. This lack of understanding is linked to a lack of learning algorithms for recurrent networks of spiking neurons (RSNNs) that are both functionally powerful and can be implemented by known biological mechanisms. Since RSNNs are simultaneously a primary target for implementations of brain-inspired circuits in neuromorphic hardware, this lack of algorithmic insight also hinders technological progress in that area. The gold standar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.09049","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":""},"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":"1901.09049","created_at":"2026-05-17T23:53:02.497985+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.09049v2","created_at":"2026-05-17T23:53:02.497985+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.09049","created_at":"2026-05-17T23:53:02.497985+00:00"},{"alias_kind":"pith_short_12","alias_value":"3TKR2CZ7XYOD","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"3TKR2CZ7XYODRVCG","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"3TKR2CZ7","created_at":"2026-05-18T12:33:10.108867+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/3TKR2CZ7XYODRVCGGXMU3ZYBGI","json":"https://pith.science/pith/3TKR2CZ7XYODRVCGGXMU3ZYBGI.json","graph_json":"https://pith.science/api/pith-number/3TKR2CZ7XYODRVCGGXMU3ZYBGI/graph.json","events_json":"https://pith.science/api/pith-number/3TKR2CZ7XYODRVCGGXMU3ZYBGI/events.json","paper":"https://pith.science/paper/3TKR2CZ7"},"agent_actions":{"view_html":"https://pith.science/pith/3TKR2CZ7XYODRVCGGXMU3ZYBGI","download_json":"https://pith.science/pith/3TKR2CZ7XYODRVCGGXMU3ZYBGI.json","view_paper":"https://pith.science/paper/3TKR2CZ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.09049&json=true","fetch_graph":"https://pith.science/api/pith-number/3TKR2CZ7XYODRVCGGXMU3ZYBGI/graph.json","fetch_events":"https://pith.science/api/pith-number/3TKR2CZ7XYODRVCGGXMU3ZYBGI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3TKR2CZ7XYODRVCGGXMU3ZYBGI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3TKR2CZ7XYODRVCGGXMU3ZYBGI/action/storage_attestation","attest_author":"https://pith.science/pith/3TKR2CZ7XYODRVCGGXMU3ZYBGI/action/author_attestation","sign_citation":"https://pith.science/pith/3TKR2CZ7XYODRVCGGXMU3ZYBGI/action/citation_signature","submit_replication":"https://pith.science/pith/3TKR2CZ7XYODRVCGGXMU3ZYBGI/action/replication_record"}},"created_at":"2026-05-17T23:53:02.497985+00:00","updated_at":"2026-05-17T23:53:02.497985+00:00"}