{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:YXIURD3ZSERNHXYBQJFPLXMXYW","short_pith_number":"pith:YXIURD3Z","schema_version":"1.0","canonical_sha256":"c5d1488f799122d3df01824af5dd97c59c1a88804d5626706c995af62fdb375a","source":{"kind":"arxiv","id":"1712.08608","version":1},"attestation_state":"computed","paper":{"title":"Learning in the Machine: the Symmetries of the Deep Learning Channel","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Peter Sadowski, Pierre Baldi, Zhiqin Lu","submitted_at":"2017-12-22T18:43:58Z","abstract_excerpt":"In a physical neural system, learning rules must be local both in space and time. In order for learning to occur, non-local information must be communicated to the deep synapses through a communication channel, the deep learning channel. We identify several possible architectures for this learning channel (Bidirectional, Conjoined, Twin, Distinct) and six symmetry challenges: 1) symmetry of architectures; 2) symmetry of weights; 3) symmetry of neurons; 4) symmetry of derivatives; 5) symmetry of processing; and 6) symmetry of learning rules. Random backpropagation (RBP) addresses the second and"},"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":"1712.08608","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-12-22T18:43:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"da8dbea5400389295b42a7ee5dce73127e5eb5d95b4790cc9836abfb6c88c851","abstract_canon_sha256":"93b72c8ebeec0c4148dd271b3cf7dc3b0e03f527c868ea64857364dfc78e6222"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:22.996535Z","signature_b64":"u+L7kN9Lm+LrsV3CMpmftLXmim8P0QvIKZesUcWlgm4PdrG/UNe9+dzEWQSRrM0wNCfxGduM3tXia//RGIDZAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5d1488f799122d3df01824af5dd97c59c1a88804d5626706c995af62fdb375a","last_reissued_at":"2026-05-18T00:27:22.996006Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:22.996006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning in the Machine: the Symmetries of the Deep Learning Channel","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Peter Sadowski, Pierre Baldi, Zhiqin Lu","submitted_at":"2017-12-22T18:43:58Z","abstract_excerpt":"In a physical neural system, learning rules must be local both in space and time. In order for learning to occur, non-local information must be communicated to the deep synapses through a communication channel, the deep learning channel. We identify several possible architectures for this learning channel (Bidirectional, Conjoined, Twin, Distinct) and six symmetry challenges: 1) symmetry of architectures; 2) symmetry of weights; 3) symmetry of neurons; 4) symmetry of derivatives; 5) symmetry of processing; and 6) symmetry of learning rules. Random backpropagation (RBP) addresses the second and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.08608","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":"1712.08608","created_at":"2026-05-18T00:27:22.996098+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.08608v1","created_at":"2026-05-18T00:27:22.996098+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.08608","created_at":"2026-05-18T00:27:22.996098+00:00"},{"alias_kind":"pith_short_12","alias_value":"YXIURD3ZSERN","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"YXIURD3ZSERNHXYB","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"YXIURD3Z","created_at":"2026-05-18T12:31:56.362134+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/YXIURD3ZSERNHXYBQJFPLXMXYW","json":"https://pith.science/pith/YXIURD3ZSERNHXYBQJFPLXMXYW.json","graph_json":"https://pith.science/api/pith-number/YXIURD3ZSERNHXYBQJFPLXMXYW/graph.json","events_json":"https://pith.science/api/pith-number/YXIURD3ZSERNHXYBQJFPLXMXYW/events.json","paper":"https://pith.science/paper/YXIURD3Z"},"agent_actions":{"view_html":"https://pith.science/pith/YXIURD3ZSERNHXYBQJFPLXMXYW","download_json":"https://pith.science/pith/YXIURD3ZSERNHXYBQJFPLXMXYW.json","view_paper":"https://pith.science/paper/YXIURD3Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.08608&json=true","fetch_graph":"https://pith.science/api/pith-number/YXIURD3ZSERNHXYBQJFPLXMXYW/graph.json","fetch_events":"https://pith.science/api/pith-number/YXIURD3ZSERNHXYBQJFPLXMXYW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YXIURD3ZSERNHXYBQJFPLXMXYW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YXIURD3ZSERNHXYBQJFPLXMXYW/action/storage_attestation","attest_author":"https://pith.science/pith/YXIURD3ZSERNHXYBQJFPLXMXYW/action/author_attestation","sign_citation":"https://pith.science/pith/YXIURD3ZSERNHXYBQJFPLXMXYW/action/citation_signature","submit_replication":"https://pith.science/pith/YXIURD3ZSERNHXYBQJFPLXMXYW/action/replication_record"}},"created_at":"2026-05-18T00:27:22.996098+00:00","updated_at":"2026-05-18T00:27:22.996098+00:00"}