{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:PBO7OLTJRLZMJDNWKHFDXP6MOF","short_pith_number":"pith:PBO7OLTJ","canonical_record":{"source":{"id":"1907.00441","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.NC","submitted_at":"2019-06-30T19:53:32Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"bcbff199dc8a9d14396c2d835936a908fd30e4e957e5dc7fca8f8f0c996866e5","abstract_canon_sha256":"2d30820d6b35612855a075475bc8f75e2543c34e6fa80b061626481cd339e194"},"schema_version":"1.0"},"canonical_sha256":"785df72e698af2c48db651ca3bbfcc714622d0a6b2a0c3154745924e73d25f9a","source":{"kind":"arxiv","id":"1907.00441","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.00441","created_at":"2026-05-17T23:41:51Z"},{"alias_kind":"arxiv_version","alias_value":"1907.00441v1","created_at":"2026-05-17T23:41:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.00441","created_at":"2026-05-17T23:41:51Z"},{"alias_kind":"pith_short_12","alias_value":"PBO7OLTJRLZM","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"PBO7OLTJRLZMJDNW","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"PBO7OLTJ","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:PBO7OLTJRLZMJDNWKHFDXP6MOF","target":"record","payload":{"canonical_record":{"source":{"id":"1907.00441","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.NC","submitted_at":"2019-06-30T19:53:32Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"bcbff199dc8a9d14396c2d835936a908fd30e4e957e5dc7fca8f8f0c996866e5","abstract_canon_sha256":"2d30820d6b35612855a075475bc8f75e2543c34e6fa80b061626481cd339e194"},"schema_version":"1.0"},"canonical_sha256":"785df72e698af2c48db651ca3bbfcc714622d0a6b2a0c3154745924e73d25f9a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:51.702208Z","signature_b64":"DkWZJ5EO/yoRuFSuQIy5uzmteoWdF5TPz6JkSEP97XDr9Yswe6rHBEslsJmRogOdKL4X86OqZlZSXakPTNNqAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"785df72e698af2c48db651ca3bbfcc714622d0a6b2a0c3154745924e73d25f9a","last_reissued_at":"2026-05-17T23:41:51.701436Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:51.701436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.00441","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":"2026-05-17T23:41:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x1QFnFuOwULXTbxwrCvnnYhN8dkQKHPs3sam1sn2FzbyZd6BLXlgBeC/bT4mnfLPyaqTHbFgy2eDfFCE/LWoBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T12:31:46.265248Z"},"content_sha256":"a331c31d48af35733d609e17b277074ebe7f9a3843a3e2fc1fa55be3206d8b91","schema_version":"1.0","event_id":"sha256:a331c31d48af35733d609e17b277074ebe7f9a3843a3e2fc1fa55be3206d8b91"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:PBO7OLTJRLZMJDNWKHFDXP6MOF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised predictive coding models may explain visual brain representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"q-bio.NC","authors_text":"Marcio Fonseca","submitted_at":"2019-06-30T19:53:32Z","abstract_excerpt":"Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states. We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations are useful to predict brain activity in the visual cortex. We use representational similarity analysis (RSA) to compare PredNet representations to functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data from the Algonauts Project. In contrast to previous findings in the literature (Khaligh-Razavi &Kriegeskorte, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.00441","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":"2026-05-17T23:41:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aPVYduZCXgCTqbaIOHtPElehsIJ+vDxgxm5YqvN61Efu/lcgNaIOWm42ASC4CitZNQWcgSi8xxS/lsnGPbnxAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T12:31:46.265620Z"},"content_sha256":"6bc06598729e9dba677f9eefc4f25af091e717641738794079fa8701236fe6f7","schema_version":"1.0","event_id":"sha256:6bc06598729e9dba677f9eefc4f25af091e717641738794079fa8701236fe6f7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PBO7OLTJRLZMJDNWKHFDXP6MOF/bundle.json","state_url":"https://pith.science/pith/PBO7OLTJRLZMJDNWKHFDXP6MOF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PBO7OLTJRLZMJDNWKHFDXP6MOF/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":"2026-06-28T12:31:46Z","links":{"resolver":"https://pith.science/pith/PBO7OLTJRLZMJDNWKHFDXP6MOF","bundle":"https://pith.science/pith/PBO7OLTJRLZMJDNWKHFDXP6MOF/bundle.json","state":"https://pith.science/pith/PBO7OLTJRLZMJDNWKHFDXP6MOF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PBO7OLTJRLZMJDNWKHFDXP6MOF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:PBO7OLTJRLZMJDNWKHFDXP6MOF","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":"2d30820d6b35612855a075475bc8f75e2543c34e6fa80b061626481cd339e194","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.NC","submitted_at":"2019-06-30T19:53:32Z","title_canon_sha256":"bcbff199dc8a9d14396c2d835936a908fd30e4e957e5dc7fca8f8f0c996866e5"},"schema_version":"1.0","source":{"id":"1907.00441","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.00441","created_at":"2026-05-17T23:41:51Z"},{"alias_kind":"arxiv_version","alias_value":"1907.00441v1","created_at":"2026-05-17T23:41:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.00441","created_at":"2026-05-17T23:41:51Z"},{"alias_kind":"pith_short_12","alias_value":"PBO7OLTJRLZM","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"PBO7OLTJRLZMJDNW","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"PBO7OLTJ","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:6bc06598729e9dba677f9eefc4f25af091e717641738794079fa8701236fe6f7","target":"graph","created_at":"2026-05-17T23:41:51Z","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":"Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states. We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations are useful to predict brain activity in the visual cortex. We use representational similarity analysis (RSA) to compare PredNet representations to functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data from the Algonauts Project. In contrast to previous findings in the literature (Khaligh-Razavi &Kriegeskorte, ","authors_text":"Marcio Fonseca","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.NC","submitted_at":"2019-06-30T19:53:32Z","title":"Unsupervised predictive coding models may explain visual brain representation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.00441","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:a331c31d48af35733d609e17b277074ebe7f9a3843a3e2fc1fa55be3206d8b91","target":"record","created_at":"2026-05-17T23:41:51Z","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":"2d30820d6b35612855a075475bc8f75e2543c34e6fa80b061626481cd339e194","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.NC","submitted_at":"2019-06-30T19:53:32Z","title_canon_sha256":"bcbff199dc8a9d14396c2d835936a908fd30e4e957e5dc7fca8f8f0c996866e5"},"schema_version":"1.0","source":{"id":"1907.00441","kind":"arxiv","version":1}},"canonical_sha256":"785df72e698af2c48db651ca3bbfcc714622d0a6b2a0c3154745924e73d25f9a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"785df72e698af2c48db651ca3bbfcc714622d0a6b2a0c3154745924e73d25f9a","first_computed_at":"2026-05-17T23:41:51.701436Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:51.701436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DkWZJ5EO/yoRuFSuQIy5uzmteoWdF5TPz6JkSEP97XDr9Yswe6rHBEslsJmRogOdKL4X86OqZlZSXakPTNNqAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:51.702208Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.00441","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a331c31d48af35733d609e17b277074ebe7f9a3843a3e2fc1fa55be3206d8b91","sha256:6bc06598729e9dba677f9eefc4f25af091e717641738794079fa8701236fe6f7"],"state_sha256":"b3589d900a69cfbb4b6826f6f3e430050f1855e5394deb3a10f02ef02f8c6299"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dykho7vrUjajmvHKwwszdLUzeU6C9enYyQnHOmLfaGIELhdEntWXK5FQv1SIDdVZXE/7GpKoO8jctfvt6uRJAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T12:31:46.267522Z","bundle_sha256":"9b08dd00e1bdbdc64c3743c434b4ee4c76d91abcc7f1d51bd4625d8158e937c3"}}