{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:U23NLFPHNM7YKQTDSATXSF5XIC","short_pith_number":"pith:U23NLFPH","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"},"canonical_sha256":"a6b6d595e76b3f85426390277917b7408655e200fb9f3d62c3be4a3cc3a23d49","source":{"kind":"arxiv","id":"1611.03000","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.03000","created_at":"2026-05-18T00:41:47Z"},{"alias_kind":"arxiv_version","alias_value":"1611.03000v4","created_at":"2026-05-18T00:41:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.03000","created_at":"2026-05-18T00:41:47Z"},{"alias_kind":"pith_short_12","alias_value":"U23NLFPHNM7Y","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"U23NLFPHNM7YKQTD","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"U23NLFPH","created_at":"2026-05-18T12:30:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:U23NLFPHNM7YKQTDSATXSF5XIC","target":"record","payload":{"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"},"canonical_sha256":"a6b6d595e76b3f85426390277917b7408655e200fb9f3d62c3be4a3cc3a23d49","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"},"source_kind":"arxiv","source_id":"1611.03000","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:41:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+7D+f52aEsmRVtQQoQEIHqGuxlJL6gE1qP67gUCuitTve9fCDBN5mioL2Fghh3hKhvR8KD72tbTMqox6luWbBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T19:02:44.730251Z"},"content_sha256":"4550147768332071a432b0f8e621c61392c99c1852263db5e6abb76d03ec075d","schema_version":"1.0","event_id":"sha256:4550147768332071a432b0f8e621c61392c99c1852263db5e6abb76d03ec075d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:U23NLFPHNM7YKQTDSATXSF5XIC","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:41:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yzn/i6QfQj5h54n4TGNWVrNQDdPj4NU78bQgocdRMVI3ziVMdwz1jZ6OZB5a1poPiXZpwib3pvhJ5phO+GyJAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T19:02:44.730926Z"},"content_sha256":"ba3f3efd068c52e319c883147dcc17e8c5a671f01cebee06c1a3c4e6df2832da","schema_version":"1.0","event_id":"sha256:ba3f3efd068c52e319c883147dcc17e8c5a671f01cebee06c1a3c4e6df2832da"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U23NLFPHNM7YKQTDSATXSF5XIC/bundle.json","state_url":"https://pith.science/pith/U23NLFPHNM7YKQTDSATXSF5XIC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U23NLFPHNM7YKQTDSATXSF5XIC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-06T19:02:44Z","links":{"resolver":"https://pith.science/pith/U23NLFPHNM7YKQTDSATXSF5XIC","bundle":"https://pith.science/pith/U23NLFPHNM7YKQTDSATXSF5XIC/bundle.json","state":"https://pith.science/pith/U23NLFPHNM7YKQTDSATXSF5XIC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U23NLFPHNM7YKQTDSATXSF5XIC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:U23NLFPHNM7YKQTDSATXSF5XIC","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8ef9a55af381dc87cfa5363793b8bf72dbb368c6892794d9ec84ee477259a256","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-09T16:25:41Z","title_canon_sha256":"a8619aebb1c217dd60bc5f910a8cbbfc4051c47282627f31ec931d9a6bf90ac1"},"schema_version":"1.0","source":{"id":"1611.03000","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.03000","created_at":"2026-05-18T00:41:47Z"},{"alias_kind":"arxiv_version","alias_value":"1611.03000v4","created_at":"2026-05-18T00:41:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.03000","created_at":"2026-05-18T00:41:47Z"},{"alias_kind":"pith_short_12","alias_value":"U23NLFPHNM7Y","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"U23NLFPHNM7YKQTD","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"U23NLFPH","created_at":"2026-05-18T12:30:46Z"}],"graph_snapshots":[{"event_id":"sha256:ba3f3efd068c52e319c883147dcc17e8c5a671f01cebee06c1a3c4e6df2832da","target":"graph","created_at":"2026-05-18T00:41:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"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","authors_text":"Amirhossein Tavanaei, Anthony S. Maida","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-09T16:25:41Z","title":"Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.03000","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4550147768332071a432b0f8e621c61392c99c1852263db5e6abb76d03ec075d","target":"record","created_at":"2026-05-18T00:41:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8ef9a55af381dc87cfa5363793b8bf72dbb368c6892794d9ec84ee477259a256","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-09T16:25:41Z","title_canon_sha256":"a8619aebb1c217dd60bc5f910a8cbbfc4051c47282627f31ec931d9a6bf90ac1"},"schema_version":"1.0","source":{"id":"1611.03000","kind":"arxiv","version":4}},"canonical_sha256":"a6b6d595e76b3f85426390277917b7408655e200fb9f3d62c3be4a3cc3a23d49","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a6b6d595e76b3f85426390277917b7408655e200fb9f3d62c3be4a3cc3a23d49","first_computed_at":"2026-05-18T00:41:47.877794Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:41:47.877794Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Fw0QuuYGYff1i4bZpfYfdbpq1vb4XfjC7HxILKDU4m4RTERgYJO0+81yOTpUizHSKJ9FX7LeAPHklUnP0zRHAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:41:47.878385Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.03000","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4550147768332071a432b0f8e621c61392c99c1852263db5e6abb76d03ec075d","sha256:ba3f3efd068c52e319c883147dcc17e8c5a671f01cebee06c1a3c4e6df2832da"],"state_sha256":"251c9a4ffc7262ed5208d8fab311f9071f467e40157818c7615bfa3af0635969"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IwrtOfR8Fj/ZPgK7pUHzVXEHI97jy43FEhO1gY7XqmEncyLii/qXNEPeuBr83Q2T4AWVKgJ2Sc5kGa5iXNY4AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T19:02:44.734785Z","bundle_sha256":"8c278d5593fe3f8b8acd9463a3ba11cb8fdcb19d8f5788c3e78245b2e2fc7b3e"}}