{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:SHHU27JW46JHZINFM5YGFY5U5L","short_pith_number":"pith:SHHU27JW","schema_version":"1.0","canonical_sha256":"91cf4d7d36e7927ca1a5677062e3b4eac8600cdbf1bd7d3d95df9ea95b273c46","source":{"kind":"arxiv","id":"1709.06206","version":1},"attestation_state":"computed","paper":{"title":"Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Chaitali Chakrabarti, Gregory K. Chen, Jae-sun Seo, Ram Krishnamurthy, Shihui Yin, Shreyas K. Venkataramanaiah, Yu Cao","submitted_at":"2017-09-19T00:05:55Z","abstract_excerpt":"We present a new back propagation based training algorithm for discrete-time spiking neural networks (SNN). Inspired by recent deep learning algorithms on binarized neural networks, binary activation with a straight-through gradient estimator is used to model the leaky integrate-fire spiking neuron, overcoming the difficulty in training SNNs using back propagation. Two SNN training algorithms are proposed: (1) SNN with discontinuous integration, which is suitable for rate-coded input spikes, and (2) SNN with continuous integration, which is more general and can handle input spikes with tempora"},"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":"1709.06206","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-09-19T00:05:55Z","cross_cats_sorted":[],"title_canon_sha256":"e219a97150895ce68220a907103f0a6b7bd5ff8ccab1eecb77148f1007ca7101","abstract_canon_sha256":"bb4fd7daa218f298024e90e01fce415eff64d7f812fb8e9bd9f8512ff0b7ad9f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:54.009383Z","signature_b64":"wL8zfSFamxGTzEOrdVwcXFyHyM+++hM5smPZWjhtyNAvZKhmmxrovL48Qp0iPv9owJxw8zJC5yVBbhCEa/orAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91cf4d7d36e7927ca1a5677062e3b4eac8600cdbf1bd7d3d95df9ea95b273c46","last_reissued_at":"2026-05-18T00:34:54.008650Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:54.008650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Chaitali Chakrabarti, Gregory K. Chen, Jae-sun Seo, Ram Krishnamurthy, Shihui Yin, Shreyas K. Venkataramanaiah, Yu Cao","submitted_at":"2017-09-19T00:05:55Z","abstract_excerpt":"We present a new back propagation based training algorithm for discrete-time spiking neural networks (SNN). Inspired by recent deep learning algorithms on binarized neural networks, binary activation with a straight-through gradient estimator is used to model the leaky integrate-fire spiking neuron, overcoming the difficulty in training SNNs using back propagation. Two SNN training algorithms are proposed: (1) SNN with discontinuous integration, which is suitable for rate-coded input spikes, and (2) SNN with continuous integration, which is more general and can handle input spikes with tempora"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.06206","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":"1709.06206","created_at":"2026-05-18T00:34:54.008775+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.06206v1","created_at":"2026-05-18T00:34:54.008775+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.06206","created_at":"2026-05-18T00:34:54.008775+00:00"},{"alias_kind":"pith_short_12","alias_value":"SHHU27JW46JH","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_16","alias_value":"SHHU27JW46JHZINF","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_8","alias_value":"SHHU27JW","created_at":"2026-05-18T12:31:43.269735+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/SHHU27JW46JHZINFM5YGFY5U5L","json":"https://pith.science/pith/SHHU27JW46JHZINFM5YGFY5U5L.json","graph_json":"https://pith.science/api/pith-number/SHHU27JW46JHZINFM5YGFY5U5L/graph.json","events_json":"https://pith.science/api/pith-number/SHHU27JW46JHZINFM5YGFY5U5L/events.json","paper":"https://pith.science/paper/SHHU27JW"},"agent_actions":{"view_html":"https://pith.science/pith/SHHU27JW46JHZINFM5YGFY5U5L","download_json":"https://pith.science/pith/SHHU27JW46JHZINFM5YGFY5U5L.json","view_paper":"https://pith.science/paper/SHHU27JW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.06206&json=true","fetch_graph":"https://pith.science/api/pith-number/SHHU27JW46JHZINFM5YGFY5U5L/graph.json","fetch_events":"https://pith.science/api/pith-number/SHHU27JW46JHZINFM5YGFY5U5L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SHHU27JW46JHZINFM5YGFY5U5L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SHHU27JW46JHZINFM5YGFY5U5L/action/storage_attestation","attest_author":"https://pith.science/pith/SHHU27JW46JHZINFM5YGFY5U5L/action/author_attestation","sign_citation":"https://pith.science/pith/SHHU27JW46JHZINFM5YGFY5U5L/action/citation_signature","submit_replication":"https://pith.science/pith/SHHU27JW46JHZINFM5YGFY5U5L/action/replication_record"}},"created_at":"2026-05-18T00:34:54.008775+00:00","updated_at":"2026-05-18T00:34:54.008775+00:00"}