{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ME6BOR7DDAJREZLPTXP3PMJ3CU","short_pith_number":"pith:ME6BOR7D","schema_version":"1.0","canonical_sha256":"613c1747e3181312656f9ddfb7b13b1514e82ca0bde1d7890cb0778950624cae","source":{"kind":"arxiv","id":"2405.20694","version":1},"attestation_state":"computed","paper":{"title":"Robust Stable Spiking Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Jianhao Ding, Tiejun Huang, Yujia Liu, Zhaofei Yu, Zhiyu Pan","submitted_at":"2024-05-31T08:40:02Z","abstract_excerpt":"Spiking neural networks (SNNs) are gaining popularity in deep learning due to their low energy budget on neuromorphic hardware. However, they still face challenges in lacking sufficient robustness to guard safety-critical applications such as autonomous driving. Many studies have been conducted to defend SNNs from the threat of adversarial attacks. This paper aims to uncover the robustness of SNN through the lens of the stability of nonlinear systems. We are inspired by the fact that searching for parameters altering the leaky integrate-and-fire dynamics can enhance their robustness. Thus, we "},"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":"2405.20694","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2024-05-31T08:40:02Z","cross_cats_sorted":[],"title_canon_sha256":"f3782702f9affc63093bc4527dc4debdcbb5f1990ae4e589349c2b6bc5d378fb","abstract_canon_sha256":"cd4c4a55c920bc55e680aca458125bc41512e412259f2bcc2a6b53b5cdb96471"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:25:41.055791Z","signature_b64":"3Ieo7q6rV4MbG0Eo2AoVRtg+sdIJpman5xO3Njon172EKtY+kQKqoj+k0O4hz8oFxrlnhE0tYB64vmsqW1GaDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"613c1747e3181312656f9ddfb7b13b1514e82ca0bde1d7890cb0778950624cae","last_reissued_at":"2026-07-05T08:25:41.055303Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:25:41.055303Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Stable Spiking Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Jianhao Ding, Tiejun Huang, Yujia Liu, Zhaofei Yu, Zhiyu Pan","submitted_at":"2024-05-31T08:40:02Z","abstract_excerpt":"Spiking neural networks (SNNs) are gaining popularity in deep learning due to their low energy budget on neuromorphic hardware. However, they still face challenges in lacking sufficient robustness to guard safety-critical applications such as autonomous driving. Many studies have been conducted to defend SNNs from the threat of adversarial attacks. This paper aims to uncover the robustness of SNN through the lens of the stability of nonlinear systems. We are inspired by the fact that searching for parameters altering the leaky integrate-and-fire dynamics can enhance their robustness. Thus, we "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.20694","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2405.20694/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":"2405.20694","created_at":"2026-07-05T08:25:41.055370+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.20694v1","created_at":"2026-07-05T08:25:41.055370+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.20694","created_at":"2026-07-05T08:25:41.055370+00:00"},{"alias_kind":"pith_short_12","alias_value":"ME6BOR7DDAJR","created_at":"2026-07-05T08:25:41.055370+00:00"},{"alias_kind":"pith_short_16","alias_value":"ME6BOR7DDAJREZLP","created_at":"2026-07-05T08:25:41.055370+00:00"},{"alias_kind":"pith_short_8","alias_value":"ME6BOR7D","created_at":"2026-07-05T08:25:41.055370+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.11134","citing_title":"Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ME6BOR7DDAJREZLPTXP3PMJ3CU","json":"https://pith.science/pith/ME6BOR7DDAJREZLPTXP3PMJ3CU.json","graph_json":"https://pith.science/api/pith-number/ME6BOR7DDAJREZLPTXP3PMJ3CU/graph.json","events_json":"https://pith.science/api/pith-number/ME6BOR7DDAJREZLPTXP3PMJ3CU/events.json","paper":"https://pith.science/paper/ME6BOR7D"},"agent_actions":{"view_html":"https://pith.science/pith/ME6BOR7DDAJREZLPTXP3PMJ3CU","download_json":"https://pith.science/pith/ME6BOR7DDAJREZLPTXP3PMJ3CU.json","view_paper":"https://pith.science/paper/ME6BOR7D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.20694&json=true","fetch_graph":"https://pith.science/api/pith-number/ME6BOR7DDAJREZLPTXP3PMJ3CU/graph.json","fetch_events":"https://pith.science/api/pith-number/ME6BOR7DDAJREZLPTXP3PMJ3CU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ME6BOR7DDAJREZLPTXP3PMJ3CU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ME6BOR7DDAJREZLPTXP3PMJ3CU/action/storage_attestation","attest_author":"https://pith.science/pith/ME6BOR7DDAJREZLPTXP3PMJ3CU/action/author_attestation","sign_citation":"https://pith.science/pith/ME6BOR7DDAJREZLPTXP3PMJ3CU/action/citation_signature","submit_replication":"https://pith.science/pith/ME6BOR7DDAJREZLPTXP3PMJ3CU/action/replication_record"}},"created_at":"2026-07-05T08:25:41.055370+00:00","updated_at":"2026-07-05T08:25:41.055370+00:00"}