{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VRLXUM6JSDUU3GPKXK5ZWYLWIH","short_pith_number":"pith:VRLXUM6J","schema_version":"1.0","canonical_sha256":"ac577a33c990e94d99eababb9b617641c7f7ee12f9d853e36dd50074200be1bb","source":{"kind":"arxiv","id":"2412.13237","version":1},"attestation_state":"computed","paper":{"title":"Optimized two-stage AI-based Neural Decoding for Enhanced Visual Stimulus Reconstruction from fMRI Data","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG","q-bio.NC"],"primary_cat":"eess.IV","authors_text":"Andrea Moglia, Lorenzo Veronese, Luca Mainardi, Pietro Cerveri","submitted_at":"2024-12-17T16:42:55Z","abstract_excerpt":"AI-based neural decoding reconstructs visual perception by leveraging generative models to map brain activity, measured through functional MRI (fMRI), into latent hierarchical representations. Traditionally, ridge linear models transform fMRI into a latent space, which is then decoded using latent diffusion models (LDM) via a pre-trained variational autoencoder (VAE). Due to the complexity and noisiness of fMRI data, newer approaches split the reconstruction into two sequential steps, the first one providing a rough visual approximation, the second on improving the stimulus prediction via LDM "},"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":"2412.13237","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2024-12-17T16:42:55Z","cross_cats_sorted":["cs.CV","cs.LG","q-bio.NC"],"title_canon_sha256":"c6cafeec2aedbecd14e672d971eb624902ef9a3112e68c782307cb83eac1ca98","abstract_canon_sha256":"9ab8dae527ee16f24b42570835aab67d878242a44e41ef49c4d7b0b9bc249731"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:04:13.538126Z","signature_b64":"zckrkKBvQWdJthm6DWamyuONBgitVwRyvrOLcRXOER7DU4otVX0PiqC7tY0J/C+PD3HXxANuDU0i6sTysvijCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac577a33c990e94d99eababb9b617641c7f7ee12f9d853e36dd50074200be1bb","last_reissued_at":"2026-07-05T12:04:13.537594Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:04:13.537594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimized two-stage AI-based Neural Decoding for Enhanced Visual Stimulus Reconstruction from fMRI Data","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG","q-bio.NC"],"primary_cat":"eess.IV","authors_text":"Andrea Moglia, Lorenzo Veronese, Luca Mainardi, Pietro Cerveri","submitted_at":"2024-12-17T16:42:55Z","abstract_excerpt":"AI-based neural decoding reconstructs visual perception by leveraging generative models to map brain activity, measured through functional MRI (fMRI), into latent hierarchical representations. Traditionally, ridge linear models transform fMRI into a latent space, which is then decoded using latent diffusion models (LDM) via a pre-trained variational autoencoder (VAE). Due to the complexity and noisiness of fMRI data, newer approaches split the reconstruction into two sequential steps, the first one providing a rough visual approximation, the second on improving the stimulus prediction via LDM "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.13237","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2412.13237/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2412.13237","created_at":"2026-07-05T12:04:13.537660+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.13237v1","created_at":"2026-07-05T12:04:13.537660+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.13237","created_at":"2026-07-05T12:04:13.537660+00:00"},{"alias_kind":"pith_short_12","alias_value":"VRLXUM6JSDUU","created_at":"2026-07-05T12:04:13.537660+00:00"},{"alias_kind":"pith_short_16","alias_value":"VRLXUM6JSDUU3GPK","created_at":"2026-07-05T12:04:13.537660+00:00"},{"alias_kind":"pith_short_8","alias_value":"VRLXUM6J","created_at":"2026-07-05T12:04:13.537660+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/VRLXUM6JSDUU3GPKXK5ZWYLWIH","json":"https://pith.science/pith/VRLXUM6JSDUU3GPKXK5ZWYLWIH.json","graph_json":"https://pith.science/api/pith-number/VRLXUM6JSDUU3GPKXK5ZWYLWIH/graph.json","events_json":"https://pith.science/api/pith-number/VRLXUM6JSDUU3GPKXK5ZWYLWIH/events.json","paper":"https://pith.science/paper/VRLXUM6J"},"agent_actions":{"view_html":"https://pith.science/pith/VRLXUM6JSDUU3GPKXK5ZWYLWIH","download_json":"https://pith.science/pith/VRLXUM6JSDUU3GPKXK5ZWYLWIH.json","view_paper":"https://pith.science/paper/VRLXUM6J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.13237&json=true","fetch_graph":"https://pith.science/api/pith-number/VRLXUM6JSDUU3GPKXK5ZWYLWIH/graph.json","fetch_events":"https://pith.science/api/pith-number/VRLXUM6JSDUU3GPKXK5ZWYLWIH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VRLXUM6JSDUU3GPKXK5ZWYLWIH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VRLXUM6JSDUU3GPKXK5ZWYLWIH/action/storage_attestation","attest_author":"https://pith.science/pith/VRLXUM6JSDUU3GPKXK5ZWYLWIH/action/author_attestation","sign_citation":"https://pith.science/pith/VRLXUM6JSDUU3GPKXK5ZWYLWIH/action/citation_signature","submit_replication":"https://pith.science/pith/VRLXUM6JSDUU3GPKXK5ZWYLWIH/action/replication_record"}},"created_at":"2026-07-05T12:04:13.537660+00:00","updated_at":"2026-07-05T12:04:13.537660+00:00"}