{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3QRUHQXM72NZMPXRDBZVCY44NH","short_pith_number":"pith:3QRUHQXM","schema_version":"1.0","canonical_sha256":"dc2343c2ecfe9b963ef1187351639c69fb2ede02ea77c99133b4f8912a99e4a0","source":{"kind":"arxiv","id":"2405.01305","version":3},"attestation_state":"computed","paper":{"title":"Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NE","authors_text":"Alpha Renner, Elisabetta Chicca, Emre Neftci, Giacomo Indiveri, Huaqiang Wu, Hugh Greatorex, Junren Chen, Madison Cotteret, Martin Ziegler","submitted_at":"2024-05-02T14:11:50Z","abstract_excerpt":"Programming recurrent spiking neural networks (RSNNs) to robustly perform multi-timescale computation remains a difficult challenge. To address this, we describe a single-shot weight learning scheme to embed robust multi-timescale dynamics into attractor-based RSNNs, by exploiting the properties of high-dimensional distributed representations. We embed finite state machines into the RSNN dynamics by superimposing a symmetric autoassociative weight matrix and asymmetric transition terms, which are each formed by the vector binding of an input and heteroassociative outer-products between states."},"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":"2405.01305","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2024-05-02T14:11:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e9b66704a9c32665c9e184cafbb5f17261a46ef02a7201f9d9af6f82f42da86a","abstract_canon_sha256":"c0db434483a4673b956d07f57cc381e1f3ee90f17f58e702e92aa9c631005db9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:59:46.269768Z","signature_b64":"lfvsSv90OvAXbfwFLaVsay0q1VxhBE2VG3Zm5DLDBdZb7QZpbBRfy5chV4SqrXpq06zByCMYd6EdBOMArThRAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc2343c2ecfe9b963ef1187351639c69fb2ede02ea77c99133b4f8912a99e4a0","last_reissued_at":"2026-07-05T09:59:46.269332Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:59:46.269332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NE","authors_text":"Alpha Renner, Elisabetta Chicca, Emre Neftci, Giacomo Indiveri, Huaqiang Wu, Hugh Greatorex, Junren Chen, Madison Cotteret, Martin Ziegler","submitted_at":"2024-05-02T14:11:50Z","abstract_excerpt":"Programming recurrent spiking neural networks (RSNNs) to robustly perform multi-timescale computation remains a difficult challenge. To address this, we describe a single-shot weight learning scheme to embed robust multi-timescale dynamics into attractor-based RSNNs, by exploiting the properties of high-dimensional distributed representations. We embed finite state machines into the RSNN dynamics by superimposing a symmetric autoassociative weight matrix and asymmetric transition terms, which are each formed by the vector binding of an input and heteroassociative outer-products between states."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.01305","kind":"arxiv","version":3},"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/2405.01305/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":"2405.01305","created_at":"2026-07-05T09:59:46.269389+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.01305v3","created_at":"2026-07-05T09:59:46.269389+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.01305","created_at":"2026-07-05T09:59:46.269389+00:00"},{"alias_kind":"pith_short_12","alias_value":"3QRUHQXM72NZ","created_at":"2026-07-05T09:59:46.269389+00:00"},{"alias_kind":"pith_short_16","alias_value":"3QRUHQXM72NZMPXR","created_at":"2026-07-05T09:59:46.269389+00:00"},{"alias_kind":"pith_short_8","alias_value":"3QRUHQXM","created_at":"2026-07-05T09:59:46.269389+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/3QRUHQXM72NZMPXRDBZVCY44NH","json":"https://pith.science/pith/3QRUHQXM72NZMPXRDBZVCY44NH.json","graph_json":"https://pith.science/api/pith-number/3QRUHQXM72NZMPXRDBZVCY44NH/graph.json","events_json":"https://pith.science/api/pith-number/3QRUHQXM72NZMPXRDBZVCY44NH/events.json","paper":"https://pith.science/paper/3QRUHQXM"},"agent_actions":{"view_html":"https://pith.science/pith/3QRUHQXM72NZMPXRDBZVCY44NH","download_json":"https://pith.science/pith/3QRUHQXM72NZMPXRDBZVCY44NH.json","view_paper":"https://pith.science/paper/3QRUHQXM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.01305&json=true","fetch_graph":"https://pith.science/api/pith-number/3QRUHQXM72NZMPXRDBZVCY44NH/graph.json","fetch_events":"https://pith.science/api/pith-number/3QRUHQXM72NZMPXRDBZVCY44NH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3QRUHQXM72NZMPXRDBZVCY44NH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3QRUHQXM72NZMPXRDBZVCY44NH/action/storage_attestation","attest_author":"https://pith.science/pith/3QRUHQXM72NZMPXRDBZVCY44NH/action/author_attestation","sign_citation":"https://pith.science/pith/3QRUHQXM72NZMPXRDBZVCY44NH/action/citation_signature","submit_replication":"https://pith.science/pith/3QRUHQXM72NZMPXRDBZVCY44NH/action/replication_record"}},"created_at":"2026-07-05T09:59:46.269389+00:00","updated_at":"2026-07-05T09:59:46.269389+00:00"}