{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YNAQZBMUHN2Y24M752EU4IE7A5","short_pith_number":"pith:YNAQZBMU","schema_version":"1.0","canonical_sha256":"c3410c85943b758d719fee894e209f07728925898025cf163561a65274ed504c","source":{"kind":"arxiv","id":"2402.14603","version":2},"attestation_state":"computed","paper":{"title":"Balanced Resonate-and-Fire Neurons","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Sander M. Bohte, Saya Higuchi, Sebastian Kairat, Sebastian Otte","submitted_at":"2024-02-02T12:57:21Z","abstract_excerpt":"The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns within the time domain due to its resonating membrane dynamics. However, previous RF formulations suffer from intrinsic shortcomings that limit effective learning and prevent exploiting the principled advantage of RF neurons. Here, we introduce the balanced RF (BRF) neuron, which alleviates some of the intrinsic limitations of vanilla RF neurons and demonstrates its effectiveness within recurrent spiking neural network"},"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":"2402.14603","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2024-02-02T12:57:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f8a3ced18012dcc8df11a00cf57d489098b2b7da48820ff37671c5b40cd7d585","abstract_canon_sha256":"f36b1497019d4e9e73d1089b8eda606c7536edf4284990e6bb1ede6137958806"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:17:37.760960Z","signature_b64":"i24LFhytcM/0Q5gezzzZA3ycm+kdVEes8ol44ri0biJ467EKDucq5x93Er7qwaYflr7kydaTqjIFbl4YuJfWDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3410c85943b758d719fee894e209f07728925898025cf163561a65274ed504c","last_reissued_at":"2026-07-05T09:17:37.760452Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:17:37.760452Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Balanced Resonate-and-Fire Neurons","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Sander M. Bohte, Saya Higuchi, Sebastian Kairat, Sebastian Otte","submitted_at":"2024-02-02T12:57:21Z","abstract_excerpt":"The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns within the time domain due to its resonating membrane dynamics. However, previous RF formulations suffer from intrinsic shortcomings that limit effective learning and prevent exploiting the principled advantage of RF neurons. Here, we introduce the balanced RF (BRF) neuron, which alleviates some of the intrinsic limitations of vanilla RF neurons and demonstrates its effectiveness within recurrent spiking neural network"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.14603","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/2402.14603/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":"2402.14603","created_at":"2026-07-05T09:17:37.760515+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.14603v2","created_at":"2026-07-05T09:17:37.760515+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.14603","created_at":"2026-07-05T09:17:37.760515+00:00"},{"alias_kind":"pith_short_12","alias_value":"YNAQZBMUHN2Y","created_at":"2026-07-05T09:17:37.760515+00:00"},{"alias_kind":"pith_short_16","alias_value":"YNAQZBMUHN2Y24M7","created_at":"2026-07-05T09:17:37.760515+00:00"},{"alias_kind":"pith_short_8","alias_value":"YNAQZBMU","created_at":"2026-07-05T09:17:37.760515+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2506.06374","citing_title":"SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YNAQZBMUHN2Y24M752EU4IE7A5","json":"https://pith.science/pith/YNAQZBMUHN2Y24M752EU4IE7A5.json","graph_json":"https://pith.science/api/pith-number/YNAQZBMUHN2Y24M752EU4IE7A5/graph.json","events_json":"https://pith.science/api/pith-number/YNAQZBMUHN2Y24M752EU4IE7A5/events.json","paper":"https://pith.science/paper/YNAQZBMU"},"agent_actions":{"view_html":"https://pith.science/pith/YNAQZBMUHN2Y24M752EU4IE7A5","download_json":"https://pith.science/pith/YNAQZBMUHN2Y24M752EU4IE7A5.json","view_paper":"https://pith.science/paper/YNAQZBMU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.14603&json=true","fetch_graph":"https://pith.science/api/pith-number/YNAQZBMUHN2Y24M752EU4IE7A5/graph.json","fetch_events":"https://pith.science/api/pith-number/YNAQZBMUHN2Y24M752EU4IE7A5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YNAQZBMUHN2Y24M752EU4IE7A5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YNAQZBMUHN2Y24M752EU4IE7A5/action/storage_attestation","attest_author":"https://pith.science/pith/YNAQZBMUHN2Y24M752EU4IE7A5/action/author_attestation","sign_citation":"https://pith.science/pith/YNAQZBMUHN2Y24M752EU4IE7A5/action/citation_signature","submit_replication":"https://pith.science/pith/YNAQZBMUHN2Y24M752EU4IE7A5/action/replication_record"}},"created_at":"2026-07-05T09:17:37.760515+00:00","updated_at":"2026-07-05T09:17:37.760515+00:00"}