{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:GOLJQ6GHTT22U2K62H55HJTCHR","short_pith_number":"pith:GOLJQ6GH","schema_version":"1.0","canonical_sha256":"33969878c79cf5aa695ed1fbd3a6623c48698d17b0f9fdfa4c94e3aae60ed308","source":{"kind":"arxiv","id":"1603.04687","version":1},"attestation_state":"computed","paper":{"title":"Recurrent Network Models Of Sequence Generation And Memory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","physics.bio-ph"],"primary_cat":"q-bio.NC","authors_text":"Christopher D Harvey, David W Tank, Kanaka Rajan","submitted_at":"2016-03-14T15:00:12Z","abstract_excerpt":"Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for sequences and memory emphasized specialized architectures in which a principled mechanism is pre-wired into their connectivity. Here we demonstrate that, starting from random connectivity and modifying a small fraction of connections, a largely disordered recur- rent network can produce sequences and implement working memory efficiently. We use this process, called Partial In-Network Training (PINning), to model and ma"},"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":"1603.04687","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.NC","submitted_at":"2016-03-14T15:00:12Z","cross_cats_sorted":["cond-mat.dis-nn","physics.bio-ph"],"title_canon_sha256":"c4f08785a9392e7b36ffd514ce78608e82a4aa4e20e874bd789a5830ad879109","abstract_canon_sha256":"676b80f399eb394c84e10ec6678eee63d814c0458a60a8c0fee7d37123af25f4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:19:04.182349Z","signature_b64":"5C+zHGN6a/sW0XIQRdHhhoJQxbhoSPOLBEXCs5v6lVw/Ic/FOVf7QCM8CfUSp84uctzXhEQSTbeJ+kTBHIF+BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33969878c79cf5aa695ed1fbd3a6623c48698d17b0f9fdfa4c94e3aae60ed308","last_reissued_at":"2026-05-18T01:19:04.181807Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:19:04.181807Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Recurrent Network Models Of Sequence Generation And Memory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","physics.bio-ph"],"primary_cat":"q-bio.NC","authors_text":"Christopher D Harvey, David W Tank, Kanaka Rajan","submitted_at":"2016-03-14T15:00:12Z","abstract_excerpt":"Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for sequences and memory emphasized specialized architectures in which a principled mechanism is pre-wired into their connectivity. Here we demonstrate that, starting from random connectivity and modifying a small fraction of connections, a largely disordered recur- rent network can produce sequences and implement working memory efficiently. We use this process, called Partial In-Network Training (PINning), to model and ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.04687","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":"1603.04687","created_at":"2026-05-18T01:19:04.181874+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.04687v1","created_at":"2026-05-18T01:19:04.181874+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.04687","created_at":"2026-05-18T01:19:04.181874+00:00"},{"alias_kind":"pith_short_12","alias_value":"GOLJQ6GHTT22","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_16","alias_value":"GOLJQ6GHTT22U2K6","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_8","alias_value":"GOLJQ6GH","created_at":"2026-05-18T12:30:19.053100+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/GOLJQ6GHTT22U2K62H55HJTCHR","json":"https://pith.science/pith/GOLJQ6GHTT22U2K62H55HJTCHR.json","graph_json":"https://pith.science/api/pith-number/GOLJQ6GHTT22U2K62H55HJTCHR/graph.json","events_json":"https://pith.science/api/pith-number/GOLJQ6GHTT22U2K62H55HJTCHR/events.json","paper":"https://pith.science/paper/GOLJQ6GH"},"agent_actions":{"view_html":"https://pith.science/pith/GOLJQ6GHTT22U2K62H55HJTCHR","download_json":"https://pith.science/pith/GOLJQ6GHTT22U2K62H55HJTCHR.json","view_paper":"https://pith.science/paper/GOLJQ6GH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.04687&json=true","fetch_graph":"https://pith.science/api/pith-number/GOLJQ6GHTT22U2K62H55HJTCHR/graph.json","fetch_events":"https://pith.science/api/pith-number/GOLJQ6GHTT22U2K62H55HJTCHR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GOLJQ6GHTT22U2K62H55HJTCHR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GOLJQ6GHTT22U2K62H55HJTCHR/action/storage_attestation","attest_author":"https://pith.science/pith/GOLJQ6GHTT22U2K62H55HJTCHR/action/author_attestation","sign_citation":"https://pith.science/pith/GOLJQ6GHTT22U2K62H55HJTCHR/action/citation_signature","submit_replication":"https://pith.science/pith/GOLJQ6GHTT22U2K62H55HJTCHR/action/replication_record"}},"created_at":"2026-05-18T01:19:04.181874+00:00","updated_at":"2026-05-18T01:19:04.181874+00:00"}