{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:UBTHUNEETNR4ENHZFPJRTDPP5H","short_pith_number":"pith:UBTHUNEE","canonical_record":{"source":{"id":"1708.04251","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-08-14T18:03:21Z","cross_cats_sorted":[],"title_canon_sha256":"01fb2aeaf45c125714cab2f3f14adc6201419dc3793db6f47fa38bc73368e578","abstract_canon_sha256":"1bfd252c51c81333cf517958120deb14044b6f292d30d9ab42b8c36de239f1ff"},"schema_version":"1.0"},"canonical_sha256":"a0667a34849b63c234f92bd3198defe9fe1f65566a10be09e3664b5810bf208b","source":{"kind":"arxiv","id":"1708.04251","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.04251","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"arxiv_version","alias_value":"1708.04251v2","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.04251","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"pith_short_12","alias_value":"UBTHUNEETNR4","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"UBTHUNEETNR4ENHZ","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"UBTHUNEE","created_at":"2026-05-18T12:31:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:UBTHUNEETNR4ENHZFPJRTDPP5H","target":"record","payload":{"canonical_record":{"source":{"id":"1708.04251","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-08-14T18:03:21Z","cross_cats_sorted":[],"title_canon_sha256":"01fb2aeaf45c125714cab2f3f14adc6201419dc3793db6f47fa38bc73368e578","abstract_canon_sha256":"1bfd252c51c81333cf517958120deb14044b6f292d30d9ab42b8c36de239f1ff"},"schema_version":"1.0"},"canonical_sha256":"a0667a34849b63c234f92bd3198defe9fe1f65566a10be09e3664b5810bf208b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:22.418036Z","signature_b64":"BWVJJpDvUPtkgLPvXzx0WE6CwRBrC6pwy4OH5vyFsTJSXeGznfp5W/yf/ln9eyC/cXazQntOdkncgwJQN9IoCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a0667a34849b63c234f92bd3198defe9fe1f65566a10be09e3664b5810bf208b","last_reissued_at":"2026-05-18T00:24:22.417633Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:22.417633Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.04251","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oCqd0TNAChp62wPOgzBezhkznUU+OJ8ETaqDWCjMA6KrNZ0t1t/mg+aJuzgei6qKd8g189GwPU41twebe50KCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T05:02:48.484786Z"},"content_sha256":"68f4068652db3ae25d3568c3ae2d3c6c19b9ad927f4dca2546c8d3b861aea464","schema_version":"1.0","event_id":"sha256:68f4068652db3ae25d3568c3ae2d3c6c19b9ad927f4dca2546c8d3b861aea464"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:UBTHUNEETNR4ENHZFPJRTDPP5H","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A learning framework for winner-take-all networks with stochastic synapses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Gert Cauwenberghs, Hesham Mostafa","submitted_at":"2017-08-14T18:03:21Z","abstract_excerpt":"Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.04251","kind":"arxiv","version":2},"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":"2026-05-18T00:24:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4kRWbeoEYTSi621guShpAP+mbKAAmCunqL5z7Yr66Mt3Wmvr1Xgz8d8iWWp+KyiED3qXgsmsKxkHZIURLWBPCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T05:02:48.485178Z"},"content_sha256":"78a7e4b18fbe933421b286e8adce879f9b4c9929951760465b44d6db7b861860","schema_version":"1.0","event_id":"sha256:78a7e4b18fbe933421b286e8adce879f9b4c9929951760465b44d6db7b861860"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UBTHUNEETNR4ENHZFPJRTDPP5H/bundle.json","state_url":"https://pith.science/pith/UBTHUNEETNR4ENHZFPJRTDPP5H/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UBTHUNEETNR4ENHZFPJRTDPP5H/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":"2026-06-02T05:02:48Z","links":{"resolver":"https://pith.science/pith/UBTHUNEETNR4ENHZFPJRTDPP5H","bundle":"https://pith.science/pith/UBTHUNEETNR4ENHZFPJRTDPP5H/bundle.json","state":"https://pith.science/pith/UBTHUNEETNR4ENHZFPJRTDPP5H/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UBTHUNEETNR4ENHZFPJRTDPP5H/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:UBTHUNEETNR4ENHZFPJRTDPP5H","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":"1bfd252c51c81333cf517958120deb14044b6f292d30d9ab42b8c36de239f1ff","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-08-14T18:03:21Z","title_canon_sha256":"01fb2aeaf45c125714cab2f3f14adc6201419dc3793db6f47fa38bc73368e578"},"schema_version":"1.0","source":{"id":"1708.04251","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.04251","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"arxiv_version","alias_value":"1708.04251v2","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.04251","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"pith_short_12","alias_value":"UBTHUNEETNR4","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"UBTHUNEETNR4ENHZ","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"UBTHUNEE","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:78a7e4b18fbe933421b286e8adce879f9b4c9929951760465b44d6db7b861860","target":"graph","created_at":"2026-05-18T00:24:22Z","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":"Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of s","authors_text":"Gert Cauwenberghs, Hesham Mostafa","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-08-14T18:03:21Z","title":"A learning framework for winner-take-all networks with stochastic synapses"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.04251","kind":"arxiv","version":2},"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":"sha256:68f4068652db3ae25d3568c3ae2d3c6c19b9ad927f4dca2546c8d3b861aea464","target":"record","created_at":"2026-05-18T00:24:22Z","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":"1bfd252c51c81333cf517958120deb14044b6f292d30d9ab42b8c36de239f1ff","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-08-14T18:03:21Z","title_canon_sha256":"01fb2aeaf45c125714cab2f3f14adc6201419dc3793db6f47fa38bc73368e578"},"schema_version":"1.0","source":{"id":"1708.04251","kind":"arxiv","version":2}},"canonical_sha256":"a0667a34849b63c234f92bd3198defe9fe1f65566a10be09e3664b5810bf208b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a0667a34849b63c234f92bd3198defe9fe1f65566a10be09e3664b5810bf208b","first_computed_at":"2026-05-18T00:24:22.417633Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:22.417633Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BWVJJpDvUPtkgLPvXzx0WE6CwRBrC6pwy4OH5vyFsTJSXeGznfp5W/yf/ln9eyC/cXazQntOdkncgwJQN9IoCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:22.418036Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.04251","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:68f4068652db3ae25d3568c3ae2d3c6c19b9ad927f4dca2546c8d3b861aea464","sha256:78a7e4b18fbe933421b286e8adce879f9b4c9929951760465b44d6db7b861860"],"state_sha256":"d14de1cb5da8f20baba3e24df688cca40cdcfc57e50b64f3babc8c19fde8b3d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q54iTkt021hGu3+ufrszhXEKTZxIl9ScSv6Yoz7R7F40DPtsY6Rc30gPHGVpqfWeqE6i5MW68fzjGD5Na+uABw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T05:02:48.487163Z","bundle_sha256":"c95db5d92af22f8be98131bf64420bc0b1156a4c3ca5daeb0caadf42a9735a6e"}}