{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SJ6WCLX4M3FC62FGIXSNU4Y4IK","short_pith_number":"pith:SJ6WCLX4","schema_version":"1.0","canonical_sha256":"927d612efc66ca2f68a645e4da731c429fa80ab252577d8a046713164c711671","source":{"kind":"arxiv","id":"2402.16763","version":2},"attestation_state":"computed","paper":{"title":"ELiSe: Efficient Learning of Sequences in Structured Recurrent Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"q-bio.NC","authors_text":"Ben von H\\\"unerbein, Federico Benitez, Kristin V\\\"olk, Laura Kriener, Mihai A. Petrovici, Walter Senn","submitted_at":"2024-02-26T17:30:34Z","abstract_excerpt":"Behavior can be described as a temporal sequence of actions driven by neural activity. To learn complex sequential patterns in neural networks, memories of past activities need to persist on significantly longer timescales than the relaxation times of single-neuron activity. While recurrent networks can produce such long transients, training these networks is a challenge. Learning via error propagation confers models such as FORCE, RTRL or BPTT a significant functional advantage, but at the expense of biological plausibility. While reservoir computing circumvents this issue by learning only th"},"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":"2402.16763","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.NC","submitted_at":"2024-02-26T17:30:34Z","cross_cats_sorted":["cs.AI","cs.NE"],"title_canon_sha256":"563770929102b40e27186ed790edfae00f6b388a6bce2d0a9cf83a8c77616e72","abstract_canon_sha256":"0a68238d1301f4ccfb2714d728387f95ac5175a3b9e3ad98548d7a6fbd893f58"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:12:26.031872Z","signature_b64":"iTARyvD0E0aOGjVS7Tcbiyue9OEWoGnAgxv/bwpjM96Vi3yA5GA/wz4KP2HlPWY1dA1Vh9kTn6GRHS4+vz9JCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"927d612efc66ca2f68a645e4da731c429fa80ab252577d8a046713164c711671","last_reissued_at":"2026-07-05T09:12:26.031392Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:12:26.031392Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ELiSe: Efficient Learning of Sequences in Structured Recurrent Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"q-bio.NC","authors_text":"Ben von H\\\"unerbein, Federico Benitez, Kristin V\\\"olk, Laura Kriener, Mihai A. Petrovici, Walter Senn","submitted_at":"2024-02-26T17:30:34Z","abstract_excerpt":"Behavior can be described as a temporal sequence of actions driven by neural activity. To learn complex sequential patterns in neural networks, memories of past activities need to persist on significantly longer timescales than the relaxation times of single-neuron activity. While recurrent networks can produce such long transients, training these networks is a challenge. Learning via error propagation confers models such as FORCE, RTRL or BPTT a significant functional advantage, but at the expense of biological plausibility. While reservoir computing circumvents this issue by learning only th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.16763","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2402.16763/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2402.16763","created_at":"2026-07-05T09:12:26.031449+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.16763v2","created_at":"2026-07-05T09:12:26.031449+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.16763","created_at":"2026-07-05T09:12:26.031449+00:00"},{"alias_kind":"pith_short_12","alias_value":"SJ6WCLX4M3FC","created_at":"2026-07-05T09:12:26.031449+00:00"},{"alias_kind":"pith_short_16","alias_value":"SJ6WCLX4M3FC62FG","created_at":"2026-07-05T09:12:26.031449+00:00"},{"alias_kind":"pith_short_8","alias_value":"SJ6WCLX4","created_at":"2026-07-05T09:12:26.031449+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.22523","citing_title":"Learning sequence timing and control of replay speed in networks of spiking neurons","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SJ6WCLX4M3FC62FGIXSNU4Y4IK","json":"https://pith.science/pith/SJ6WCLX4M3FC62FGIXSNU4Y4IK.json","graph_json":"https://pith.science/api/pith-number/SJ6WCLX4M3FC62FGIXSNU4Y4IK/graph.json","events_json":"https://pith.science/api/pith-number/SJ6WCLX4M3FC62FGIXSNU4Y4IK/events.json","paper":"https://pith.science/paper/SJ6WCLX4"},"agent_actions":{"view_html":"https://pith.science/pith/SJ6WCLX4M3FC62FGIXSNU4Y4IK","download_json":"https://pith.science/pith/SJ6WCLX4M3FC62FGIXSNU4Y4IK.json","view_paper":"https://pith.science/paper/SJ6WCLX4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.16763&json=true","fetch_graph":"https://pith.science/api/pith-number/SJ6WCLX4M3FC62FGIXSNU4Y4IK/graph.json","fetch_events":"https://pith.science/api/pith-number/SJ6WCLX4M3FC62FGIXSNU4Y4IK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SJ6WCLX4M3FC62FGIXSNU4Y4IK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SJ6WCLX4M3FC62FGIXSNU4Y4IK/action/storage_attestation","attest_author":"https://pith.science/pith/SJ6WCLX4M3FC62FGIXSNU4Y4IK/action/author_attestation","sign_citation":"https://pith.science/pith/SJ6WCLX4M3FC62FGIXSNU4Y4IK/action/citation_signature","submit_replication":"https://pith.science/pith/SJ6WCLX4M3FC62FGIXSNU4Y4IK/action/replication_record"}},"created_at":"2026-07-05T09:12:26.031449+00:00","updated_at":"2026-07-05T09:12:26.031449+00:00"}