{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:3ASH7Y7DQIHRLNE3WABH4ZVIZT","short_pith_number":"pith:3ASH7Y7D","schema_version":"1.0","canonical_sha256":"d8247fe3e3820f15b49bb0027e66a8ccd0c417ec78d23ff67fafdbe0a441d0b6","source":{"kind":"arxiv","id":"1810.08646","version":1},"attestation_state":"computed","paper":{"title":"SLAYER: Spike Layer Error Reassignment in Time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.NE","authors_text":"Garrick Orchard, Sumit Bam Shrestha","submitted_at":"2018-09-05T10:10:03Z","abstract_excerpt":"Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue for low power spike event based computation. However, the spike generation function is non-differentiable and therefore not directly compatible with the standard error backpropagation algorithm. In this paper, we introduce a new general backpropagation mechanism for learning synaptic weights and axonal delays which overcomes the problem of non-differentiability of the spike function and uses a temporal credit assignment policy for backpropagating error to preceding layers. We describe and release a GPU accelerated s"},"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":"1810.08646","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-09-05T10:10:03Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"cf38bcf856ef2cd118c87d4d827d0a62fa4d187ffadef323610d1c43d6694a0e","abstract_canon_sha256":"debe34a832c03d4c96a29c17ba4d67349df8f52af2178dfd179a82084df5e8bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:43.932508Z","signature_b64":"Q3qTLkIPGLByXfjQ7nyjJ7k0V9H9LYQXc4ANJXiIf2GI+zd9o2YjDsmkEOEGc5rjqtdm4XI+mng75CPHkP3VCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8247fe3e3820f15b49bb0027e66a8ccd0c417ec78d23ff67fafdbe0a441d0b6","last_reissued_at":"2026-05-18T00:02:43.932087Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:43.932087Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SLAYER: Spike Layer Error Reassignment in Time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.NE","authors_text":"Garrick Orchard, Sumit Bam Shrestha","submitted_at":"2018-09-05T10:10:03Z","abstract_excerpt":"Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue for low power spike event based computation. However, the spike generation function is non-differentiable and therefore not directly compatible with the standard error backpropagation algorithm. In this paper, we introduce a new general backpropagation mechanism for learning synaptic weights and axonal delays which overcomes the problem of non-differentiability of the spike function and uses a temporal credit assignment policy for backpropagating error to preceding layers. We describe and release a GPU accelerated s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.08646","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":"1810.08646","created_at":"2026-05-18T00:02:43.932142+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.08646v1","created_at":"2026-05-18T00:02:43.932142+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.08646","created_at":"2026-05-18T00:02:43.932142+00:00"},{"alias_kind":"pith_short_12","alias_value":"3ASH7Y7DQIHR","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"3ASH7Y7DQIHRLNE3","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"3ASH7Y7D","created_at":"2026-05-18T12:32:02.567920+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2511.08558","citing_title":"Hyperdimensional Decoding of Spiking Neural Networks","ref_index":56,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3ASH7Y7DQIHRLNE3WABH4ZVIZT","json":"https://pith.science/pith/3ASH7Y7DQIHRLNE3WABH4ZVIZT.json","graph_json":"https://pith.science/api/pith-number/3ASH7Y7DQIHRLNE3WABH4ZVIZT/graph.json","events_json":"https://pith.science/api/pith-number/3ASH7Y7DQIHRLNE3WABH4ZVIZT/events.json","paper":"https://pith.science/paper/3ASH7Y7D"},"agent_actions":{"view_html":"https://pith.science/pith/3ASH7Y7DQIHRLNE3WABH4ZVIZT","download_json":"https://pith.science/pith/3ASH7Y7DQIHRLNE3WABH4ZVIZT.json","view_paper":"https://pith.science/paper/3ASH7Y7D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.08646&json=true","fetch_graph":"https://pith.science/api/pith-number/3ASH7Y7DQIHRLNE3WABH4ZVIZT/graph.json","fetch_events":"https://pith.science/api/pith-number/3ASH7Y7DQIHRLNE3WABH4ZVIZT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3ASH7Y7DQIHRLNE3WABH4ZVIZT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3ASH7Y7DQIHRLNE3WABH4ZVIZT/action/storage_attestation","attest_author":"https://pith.science/pith/3ASH7Y7DQIHRLNE3WABH4ZVIZT/action/author_attestation","sign_citation":"https://pith.science/pith/3ASH7Y7DQIHRLNE3WABH4ZVIZT/action/citation_signature","submit_replication":"https://pith.science/pith/3ASH7Y7DQIHRLNE3WABH4ZVIZT/action/replication_record"}},"created_at":"2026-05-18T00:02:43.932142+00:00","updated_at":"2026-05-18T00:02:43.932142+00:00"}