{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:UFK7GPRKOHQAJ7N7FDVHUNU5BE","short_pith_number":"pith:UFK7GPRK","canonical_record":{"source":{"id":"1612.03214","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-09T23:17:11Z","cross_cats_sorted":["cs.NE","q-bio.NC"],"title_canon_sha256":"400c96a74bf77bdb25e6238c5c062826b2d27e1e6309c3affcc9897070c97a5e","abstract_canon_sha256":"f452a34bb51b1207645fff60fff25fe558232069f813f05ccb602ebb715b6c56"},"schema_version":"1.0"},"canonical_sha256":"a155f33e2a71e004fdbf28ea7a369d0936837cc62fc830772eb3d48640bf5762","source":{"kind":"arxiv","id":"1612.03214","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.03214","created_at":"2026-05-18T00:54:52Z"},{"alias_kind":"arxiv_version","alias_value":"1612.03214v1","created_at":"2026-05-18T00:54:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.03214","created_at":"2026-05-18T00:54:52Z"},{"alias_kind":"pith_short_12","alias_value":"UFK7GPRKOHQA","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"UFK7GPRKOHQAJ7N7","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"UFK7GPRK","created_at":"2026-05-18T12:30:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:UFK7GPRKOHQAJ7N7FDVHUNU5BE","target":"record","payload":{"canonical_record":{"source":{"id":"1612.03214","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-09T23:17:11Z","cross_cats_sorted":["cs.NE","q-bio.NC"],"title_canon_sha256":"400c96a74bf77bdb25e6238c5c062826b2d27e1e6309c3affcc9897070c97a5e","abstract_canon_sha256":"f452a34bb51b1207645fff60fff25fe558232069f813f05ccb602ebb715b6c56"},"schema_version":"1.0"},"canonical_sha256":"a155f33e2a71e004fdbf28ea7a369d0936837cc62fc830772eb3d48640bf5762","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:54:52.427210Z","signature_b64":"cBDZFfiv43J8w4iOAnV0Dlm0AhnXf9kMtdH3PT8+XCWi+OpxSFSIiv+4NTf2AKn4l4aU87f2pXbTeoQKF6A8Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a155f33e2a71e004fdbf28ea7a369d0936837cc62fc830772eb3d48640bf5762","last_reissued_at":"2026-05-18T00:54:52.426753Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:54:52.426753Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.03214","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:54:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8GwjnIJIC8fo57XHpm270mPYg66R0djsg4P13ZVv51O4W3XdqnDca6szngEuzDcz8+h+dmN21BRkiXdT/cnCAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T19:42:34.814841Z"},"content_sha256":"bb6b0a82e4e7f9c1e86f003d53b59acb802967e3b859618ed6edcfa9d04c093f","schema_version":"1.0","event_id":"sha256:bb6b0a82e4e7f9c1e86f003d53b59acb802967e3b859618ed6edcfa9d04c093f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:UFK7GPRKOHQAJ7N7FDVHUNU5BE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","q-bio.NC"],"primary_cat":"cs.LG","authors_text":"Johanni Brea, Thomas Mesnard, Wulfram Gerstner","submitted_at":"2016-12-09T23:17:11Z","abstract_excerpt":"In machine learning, error back-propagation in multi-layer neural networks (deep learning) has been impressively successful in supervised and reinforcement learning tasks. As a model for learning in the brain, however, deep learning has long been regarded as implausible, since it relies in its basic form on a non-local plasticity rule. To overcome this problem, energy-based models with local contrastive Hebbian learning were proposed and tested on a classification task with networks of rate neurons. We extended this work by implementing and testing such a model with networks of leaky integrate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.03214","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:54:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NcIXMSOVCYWTh4C7Fo1SiBkDKaveC5LQ0Xm5pUmyAWKN26MaYaVBy0/EBqWA94eZs8miWaAGIry9v1STnPrEDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T19:42:34.815580Z"},"content_sha256":"56cc1cd062a36be67dad97a6f185c4ccbd7df758acf0c372336e128141c64c66","schema_version":"1.0","event_id":"sha256:56cc1cd062a36be67dad97a6f185c4ccbd7df758acf0c372336e128141c64c66"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UFK7GPRKOHQAJ7N7FDVHUNU5BE/bundle.json","state_url":"https://pith.science/pith/UFK7GPRKOHQAJ7N7FDVHUNU5BE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UFK7GPRKOHQAJ7N7FDVHUNU5BE/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":"2026-06-08T19:42:34Z","links":{"resolver":"https://pith.science/pith/UFK7GPRKOHQAJ7N7FDVHUNU5BE","bundle":"https://pith.science/pith/UFK7GPRKOHQAJ7N7FDVHUNU5BE/bundle.json","state":"https://pith.science/pith/UFK7GPRKOHQAJ7N7FDVHUNU5BE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UFK7GPRKOHQAJ7N7FDVHUNU5BE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:UFK7GPRKOHQAJ7N7FDVHUNU5BE","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":"f452a34bb51b1207645fff60fff25fe558232069f813f05ccb602ebb715b6c56","cross_cats_sorted":["cs.NE","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-09T23:17:11Z","title_canon_sha256":"400c96a74bf77bdb25e6238c5c062826b2d27e1e6309c3affcc9897070c97a5e"},"schema_version":"1.0","source":{"id":"1612.03214","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.03214","created_at":"2026-05-18T00:54:52Z"},{"alias_kind":"arxiv_version","alias_value":"1612.03214v1","created_at":"2026-05-18T00:54:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.03214","created_at":"2026-05-18T00:54:52Z"},{"alias_kind":"pith_short_12","alias_value":"UFK7GPRKOHQA","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"UFK7GPRKOHQAJ7N7","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"UFK7GPRK","created_at":"2026-05-18T12:30:46Z"}],"graph_snapshots":[{"event_id":"sha256:56cc1cd062a36be67dad97a6f185c4ccbd7df758acf0c372336e128141c64c66","target":"graph","created_at":"2026-05-18T00:54:52Z","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":"In machine learning, error back-propagation in multi-layer neural networks (deep learning) has been impressively successful in supervised and reinforcement learning tasks. As a model for learning in the brain, however, deep learning has long been regarded as implausible, since it relies in its basic form on a non-local plasticity rule. To overcome this problem, energy-based models with local contrastive Hebbian learning were proposed and tested on a classification task with networks of rate neurons. We extended this work by implementing and testing such a model with networks of leaky integrate","authors_text":"Johanni Brea, Thomas Mesnard, Wulfram Gerstner","cross_cats":["cs.NE","q-bio.NC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-09T23:17:11Z","title":"Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.03214","kind":"arxiv","version":1},"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:bb6b0a82e4e7f9c1e86f003d53b59acb802967e3b859618ed6edcfa9d04c093f","target":"record","created_at":"2026-05-18T00:54:52Z","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":"f452a34bb51b1207645fff60fff25fe558232069f813f05ccb602ebb715b6c56","cross_cats_sorted":["cs.NE","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-09T23:17:11Z","title_canon_sha256":"400c96a74bf77bdb25e6238c5c062826b2d27e1e6309c3affcc9897070c97a5e"},"schema_version":"1.0","source":{"id":"1612.03214","kind":"arxiv","version":1}},"canonical_sha256":"a155f33e2a71e004fdbf28ea7a369d0936837cc62fc830772eb3d48640bf5762","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a155f33e2a71e004fdbf28ea7a369d0936837cc62fc830772eb3d48640bf5762","first_computed_at":"2026-05-18T00:54:52.426753Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:54:52.426753Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cBDZFfiv43J8w4iOAnV0Dlm0AhnXf9kMtdH3PT8+XCWi+OpxSFSIiv+4NTf2AKn4l4aU87f2pXbTeoQKF6A8Cw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:54:52.427210Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.03214","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bb6b0a82e4e7f9c1e86f003d53b59acb802967e3b859618ed6edcfa9d04c093f","sha256:56cc1cd062a36be67dad97a6f185c4ccbd7df758acf0c372336e128141c64c66"],"state_sha256":"bb41fcf0952aea868b0b974add103cee37cfcc80fb2f5a613eaf62d338fce57b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qcMH0tTUT9l4L8feX9HjLlscSYWtGyDqjip5ALTI8O6jEWyNsUk47Du3gDwMCU/hZ+cOihDEMD3sUJwt2z3EAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T19:42:34.820053Z","bundle_sha256":"03f40edb31ce2f8ba681ce399587729f0c535b9f9f67f8b3fe057a832261628c"}}