{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OKNWYTAJ23Q3W4Y7VVM2VEFGI7","short_pith_number":"pith:OKNWYTAJ","schema_version":"1.0","canonical_sha256":"729b6c4c09d6e1bb731fad59aa90a647e1775bd7731f6ec286669d9097ac7b60","source":{"kind":"arxiv","id":"2503.02013","version":1},"attestation_state":"computed","paper":{"title":"Sustainable AI: Mathematical Foundations of Spiking Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Adalbert Fono, Ernesto Araya, Gitta Kutyniok, Holger Boche, Manjot Singh, Philipp C. Petersen","submitted_at":"2025-03-03T19:44:12Z","abstract_excerpt":"Deep learning's success comes with growing energy demands, raising concerns about the long-term sustainability of the field. Spiking neural networks, inspired by biological neurons, offer a promising alternative with potential computational and energy-efficiency gains. This article examines the computational properties of spiking networks through the lens of learning theory, focusing on expressivity, training, and generalization, as well as energy-efficient implementations while comparing them to artificial neural networks. By categorizing spiking models based on time representation and inform"},"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":"2503.02013","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2025-03-03T19:44:12Z","cross_cats_sorted":[],"title_canon_sha256":"25ea9b1ace730bd69b17dfb20a5e7da5ee3e4634087a9dcac7cb40495bfe2e3b","abstract_canon_sha256":"3dcf0b86f8886b5ba89b7d180ba2636fb6621064f65da1bd5b2982d82e9eaa6f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:23:34.485814Z","signature_b64":"y4HXiFkLnujMwq3EuP4+Z8vWpWIbk1RZU+Ax1nwG33O0lxnkMbi9UZn2wQpXF7IHHbM/h5fYCb5zCK48C4L9Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"729b6c4c09d6e1bb731fad59aa90a647e1775bd7731f6ec286669d9097ac7b60","last_reissued_at":"2026-07-05T10:23:34.485269Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:23:34.485269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sustainable AI: Mathematical Foundations of Spiking Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Adalbert Fono, Ernesto Araya, Gitta Kutyniok, Holger Boche, Manjot Singh, Philipp C. Petersen","submitted_at":"2025-03-03T19:44:12Z","abstract_excerpt":"Deep learning's success comes with growing energy demands, raising concerns about the long-term sustainability of the field. Spiking neural networks, inspired by biological neurons, offer a promising alternative with potential computational and energy-efficiency gains. This article examines the computational properties of spiking networks through the lens of learning theory, focusing on expressivity, training, and generalization, as well as energy-efficient implementations while comparing them to artificial neural networks. By categorizing spiking models based on time representation and inform"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.02013","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/2503.02013/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":"2503.02013","created_at":"2026-07-05T10:23:34.485359+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.02013v1","created_at":"2026-07-05T10:23:34.485359+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.02013","created_at":"2026-07-05T10:23:34.485359+00:00"},{"alias_kind":"pith_short_12","alias_value":"OKNWYTAJ23Q3","created_at":"2026-07-05T10:23:34.485359+00:00"},{"alias_kind":"pith_short_16","alias_value":"OKNWYTAJ23Q3W4Y7","created_at":"2026-07-05T10:23:34.485359+00:00"},{"alias_kind":"pith_short_8","alias_value":"OKNWYTAJ","created_at":"2026-07-05T10:23:34.485359+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.09790","citing_title":"Complexity Theory meets Ordinary Differential Equations","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OKNWYTAJ23Q3W4Y7VVM2VEFGI7","json":"https://pith.science/pith/OKNWYTAJ23Q3W4Y7VVM2VEFGI7.json","graph_json":"https://pith.science/api/pith-number/OKNWYTAJ23Q3W4Y7VVM2VEFGI7/graph.json","events_json":"https://pith.science/api/pith-number/OKNWYTAJ23Q3W4Y7VVM2VEFGI7/events.json","paper":"https://pith.science/paper/OKNWYTAJ"},"agent_actions":{"view_html":"https://pith.science/pith/OKNWYTAJ23Q3W4Y7VVM2VEFGI7","download_json":"https://pith.science/pith/OKNWYTAJ23Q3W4Y7VVM2VEFGI7.json","view_paper":"https://pith.science/paper/OKNWYTAJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.02013&json=true","fetch_graph":"https://pith.science/api/pith-number/OKNWYTAJ23Q3W4Y7VVM2VEFGI7/graph.json","fetch_events":"https://pith.science/api/pith-number/OKNWYTAJ23Q3W4Y7VVM2VEFGI7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OKNWYTAJ23Q3W4Y7VVM2VEFGI7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OKNWYTAJ23Q3W4Y7VVM2VEFGI7/action/storage_attestation","attest_author":"https://pith.science/pith/OKNWYTAJ23Q3W4Y7VVM2VEFGI7/action/author_attestation","sign_citation":"https://pith.science/pith/OKNWYTAJ23Q3W4Y7VVM2VEFGI7/action/citation_signature","submit_replication":"https://pith.science/pith/OKNWYTAJ23Q3W4Y7VVM2VEFGI7/action/replication_record"}},"created_at":"2026-07-05T10:23:34.485359+00:00","updated_at":"2026-07-05T10:23:34.485359+00:00"}