{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:U23NLFPHNM7YKQTDSATXSF5XIC","short_pith_number":"pith:U23NLFPH","schema_version":"1.0","canonical_sha256":"a6b6d595e76b3f85426390277917b7408655e200fb9f3d62c3be4a3cc3a23d49","source":{"kind":"arxiv","id":"1611.03000","version":4},"attestation_state":"computed","paper":{"title":"Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Amirhossein Tavanaei, Anthony S. Maida","submitted_at":"2016-11-09T16:25:41Z","abstract_excerpt":"Hierarchical feature discovery using non-spiking convolutional neural networks (CNNs) has attracted much recent interest in machine learning and computer vision. However, it is still not well understood how to create a biologically plausible network of brain-like, spiking neurons with multi-layer, unsupervised learning. This paper explores a novel bio-inspired spiking CNN that is trained in a greedy, layer-wise fashion. The proposed network consists of a spiking convolutional-pooling layer followed by a feature discovery layer extracting independent visual features. Kernels for the convolution"},"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":"1611.03000","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-09T16:25:41Z","cross_cats_sorted":[],"title_canon_sha256":"a8619aebb1c217dd60bc5f910a8cbbfc4051c47282627f31ec931d9a6bf90ac1","abstract_canon_sha256":"8ef9a55af381dc87cfa5363793b8bf72dbb368c6892794d9ec84ee477259a256"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:47.878385Z","signature_b64":"Fw0QuuYGYff1i4bZpfYfdbpq1vb4XfjC7HxILKDU4m4RTERgYJO0+81yOTpUizHSKJ9FX7LeAPHklUnP0zRHAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6b6d595e76b3f85426390277917b7408655e200fb9f3d62c3be4a3cc3a23d49","last_reissued_at":"2026-05-18T00:41:47.877794Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:47.877794Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Amirhossein Tavanaei, Anthony S. Maida","submitted_at":"2016-11-09T16:25:41Z","abstract_excerpt":"Hierarchical feature discovery using non-spiking convolutional neural networks (CNNs) has attracted much recent interest in machine learning and computer vision. However, it is still not well understood how to create a biologically plausible network of brain-like, spiking neurons with multi-layer, unsupervised learning. This paper explores a novel bio-inspired spiking CNN that is trained in a greedy, layer-wise fashion. The proposed network consists of a spiking convolutional-pooling layer followed by a feature discovery layer extracting independent visual features. Kernels for the convolution"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.03000","kind":"arxiv","version":4},"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":"1611.03000","created_at":"2026-05-18T00:41:47.877872+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.03000v4","created_at":"2026-05-18T00:41:47.877872+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.03000","created_at":"2026-05-18T00:41:47.877872+00:00"},{"alias_kind":"pith_short_12","alias_value":"U23NLFPHNM7Y","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_16","alias_value":"U23NLFPHNM7YKQTD","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_8","alias_value":"U23NLFPH","created_at":"2026-05-18T12:30:46.583412+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/U23NLFPHNM7YKQTDSATXSF5XIC","json":"https://pith.science/pith/U23NLFPHNM7YKQTDSATXSF5XIC.json","graph_json":"https://pith.science/api/pith-number/U23NLFPHNM7YKQTDSATXSF5XIC/graph.json","events_json":"https://pith.science/api/pith-number/U23NLFPHNM7YKQTDSATXSF5XIC/events.json","paper":"https://pith.science/paper/U23NLFPH"},"agent_actions":{"view_html":"https://pith.science/pith/U23NLFPHNM7YKQTDSATXSF5XIC","download_json":"https://pith.science/pith/U23NLFPHNM7YKQTDSATXSF5XIC.json","view_paper":"https://pith.science/paper/U23NLFPH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.03000&json=true","fetch_graph":"https://pith.science/api/pith-number/U23NLFPHNM7YKQTDSATXSF5XIC/graph.json","fetch_events":"https://pith.science/api/pith-number/U23NLFPHNM7YKQTDSATXSF5XIC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U23NLFPHNM7YKQTDSATXSF5XIC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U23NLFPHNM7YKQTDSATXSF5XIC/action/storage_attestation","attest_author":"https://pith.science/pith/U23NLFPHNM7YKQTDSATXSF5XIC/action/author_attestation","sign_citation":"https://pith.science/pith/U23NLFPHNM7YKQTDSATXSF5XIC/action/citation_signature","submit_replication":"https://pith.science/pith/U23NLFPHNM7YKQTDSATXSF5XIC/action/replication_record"}},"created_at":"2026-05-18T00:41:47.877872+00:00","updated_at":"2026-05-18T00:41:47.877872+00:00"}