{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SZCSQLEPXW3ZYHETUKML4HDZH5","short_pith_number":"pith:SZCSQLEP","schema_version":"1.0","canonical_sha256":"9645282c8fbdb79c1c93a298be1c793f65044f700b35c9ee19398c68effa10e5","source":{"kind":"arxiv","id":"2409.18462","version":1},"attestation_state":"computed","paper":{"title":"Latent Representation Learning for Multimodal Brain Activity Translation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.NC"],"primary_cat":"cs.LG","authors_text":"Arman Afrasiyabi, Dhananjay Bhaskar, Erica L. Busch, Guillaume Lajoie, Laurent Caplette, Nicholas B. Turk-Browne, Rahul Singh, Smita Krishnaswamy","submitted_at":"2024-09-27T05:50:29Z","abstract_excerpt":"Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent"},"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":"2409.18462","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-27T05:50:29Z","cross_cats_sorted":["q-bio.NC"],"title_canon_sha256":"01151cfaffb7b3d5d55fdf588074b6978201763e2c466269aafa76b1210760f5","abstract_canon_sha256":"7a78cc3367b4074a6c8f58b4912efc85ae795a5d0582a8a53be23dc287ce67af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:12:36.207396Z","signature_b64":"qVEIbyNvUsdFR50R2p0VIuy3NtCI3iOwImDHRpCNBd1khkHSoSLAwvk7lncy5MhyckbaELNmtem6jL1gu/xDAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9645282c8fbdb79c1c93a298be1c793f65044f700b35c9ee19398c68effa10e5","last_reissued_at":"2026-07-05T09:12:36.206969Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:12:36.206969Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Latent Representation Learning for Multimodal Brain Activity Translation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.NC"],"primary_cat":"cs.LG","authors_text":"Arman Afrasiyabi, Dhananjay Bhaskar, Erica L. Busch, Guillaume Lajoie, Laurent Caplette, Nicholas B. Turk-Browne, Rahul Singh, Smita Krishnaswamy","submitted_at":"2024-09-27T05:50:29Z","abstract_excerpt":"Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.18462","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/2409.18462/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":"2409.18462","created_at":"2026-07-05T09:12:36.207025+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.18462v1","created_at":"2026-07-05T09:12:36.207025+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.18462","created_at":"2026-07-05T09:12:36.207025+00:00"},{"alias_kind":"pith_short_12","alias_value":"SZCSQLEPXW3Z","created_at":"2026-07-05T09:12:36.207025+00:00"},{"alias_kind":"pith_short_16","alias_value":"SZCSQLEPXW3ZYHET","created_at":"2026-07-05T09:12:36.207025+00:00"},{"alias_kind":"pith_short_8","alias_value":"SZCSQLEP","created_at":"2026-07-05T09:12:36.207025+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/SZCSQLEPXW3ZYHETUKML4HDZH5","json":"https://pith.science/pith/SZCSQLEPXW3ZYHETUKML4HDZH5.json","graph_json":"https://pith.science/api/pith-number/SZCSQLEPXW3ZYHETUKML4HDZH5/graph.json","events_json":"https://pith.science/api/pith-number/SZCSQLEPXW3ZYHETUKML4HDZH5/events.json","paper":"https://pith.science/paper/SZCSQLEP"},"agent_actions":{"view_html":"https://pith.science/pith/SZCSQLEPXW3ZYHETUKML4HDZH5","download_json":"https://pith.science/pith/SZCSQLEPXW3ZYHETUKML4HDZH5.json","view_paper":"https://pith.science/paper/SZCSQLEP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.18462&json=true","fetch_graph":"https://pith.science/api/pith-number/SZCSQLEPXW3ZYHETUKML4HDZH5/graph.json","fetch_events":"https://pith.science/api/pith-number/SZCSQLEPXW3ZYHETUKML4HDZH5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SZCSQLEPXW3ZYHETUKML4HDZH5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SZCSQLEPXW3ZYHETUKML4HDZH5/action/storage_attestation","attest_author":"https://pith.science/pith/SZCSQLEPXW3ZYHETUKML4HDZH5/action/author_attestation","sign_citation":"https://pith.science/pith/SZCSQLEPXW3ZYHETUKML4HDZH5/action/citation_signature","submit_replication":"https://pith.science/pith/SZCSQLEPXW3ZYHETUKML4HDZH5/action/replication_record"}},"created_at":"2026-07-05T09:12:36.207025+00:00","updated_at":"2026-07-05T09:12:36.207025+00:00"}