{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PYH2DTSNGM7D6MK7SNFEWW6J5M","short_pith_number":"pith:PYH2DTSN","schema_version":"1.0","canonical_sha256":"7e0fa1ce4d333e3f315f934a4b5bc9eb28a44ea3d6cd3f52e4ff80b092d067d0","source":{"kind":"arxiv","id":"2504.08201","version":4},"attestation_state":"computed","paper":{"title":"Neural Encoding and Decoding at Scale","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"q-bio.NC","authors_text":"Alexandre Andre, Cole Hurwitz, Eva Dyer, Hanrui Lyu, Liam Paninski, Mehdi Azabou, The International Brain Laboratory, Yanchen Wang, Yizi Zhang, Zixuan Wang","submitted_at":"2025-04-11T02:06:20Z","abstract_excerpt":"Recent work has demonstrated that large-scale, multi-animal models are powerful tools for characterizing the relationship between neural activity and behavior. Current large-scale approaches, however, focus exclusively on either predicting neural activity from behavior (encoding) or predicting behavior from neural activity (decoding), limiting their ability to capture the bidirectional relationship between neural activity and behavior. To bridge this gap, we introduce a multimodal, multi-task model that enables simultaneous Neural Encoding and Decoding at Scale (NEDS). Central to our approach "},"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":"2504.08201","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.NC","submitted_at":"2025-04-11T02:06:20Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"926422e9442bec23f7503da67bfbc7332875212249c9a4250186faf1c530341f","abstract_canon_sha256":"036282bb381db6195274dc7d13d829cb4141413af27561e31db73e642eeaea67"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:09:11.276663Z","signature_b64":"gcmBzjAgz3TVDxS1FQ45Sw6YO0BzC29+/sRvmWSlL2Zza2NlqxwBAu45jOa6b7wbBQheQJmVD+02+U+XjYmcAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e0fa1ce4d333e3f315f934a4b5bc9eb28a44ea3d6cd3f52e4ff80b092d067d0","last_reissued_at":"2026-07-05T11:09:11.276173Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:09:11.276173Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Encoding and Decoding at Scale","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"q-bio.NC","authors_text":"Alexandre Andre, Cole Hurwitz, Eva Dyer, Hanrui Lyu, Liam Paninski, Mehdi Azabou, The International Brain Laboratory, Yanchen Wang, Yizi Zhang, Zixuan Wang","submitted_at":"2025-04-11T02:06:20Z","abstract_excerpt":"Recent work has demonstrated that large-scale, multi-animal models are powerful tools for characterizing the relationship between neural activity and behavior. Current large-scale approaches, however, focus exclusively on either predicting neural activity from behavior (encoding) or predicting behavior from neural activity (decoding), limiting their ability to capture the bidirectional relationship between neural activity and behavior. To bridge this gap, we introduce a multimodal, multi-task model that enables simultaneous Neural Encoding and Decoding at Scale (NEDS). Central to our approach "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.08201","kind":"arxiv","version":4},"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/2504.08201/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":"2504.08201","created_at":"2026-07-05T11:09:11.276233+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.08201v4","created_at":"2026-07-05T11:09:11.276233+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.08201","created_at":"2026-07-05T11:09:11.276233+00:00"},{"alias_kind":"pith_short_12","alias_value":"PYH2DTSNGM7D","created_at":"2026-07-05T11:09:11.276233+00:00"},{"alias_kind":"pith_short_16","alias_value":"PYH2DTSNGM7D6MK7","created_at":"2026-07-05T11:09:11.276233+00:00"},{"alias_kind":"pith_short_8","alias_value":"PYH2DTSN","created_at":"2026-07-05T11:09:11.276233+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22182","citing_title":"Dual-Stream EEG Decoding for 3D Visual Perception","ref_index":105,"is_internal_anchor":false},{"citing_arxiv_id":"2511.21740","citing_title":"A cross-species neural foundation model for end-to-end speech decoding","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04958","citing_title":"CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00061","citing_title":"UniBCI: Towards a Unified Pretrained Model for Invasive Brain-Computer Interfaces","ref_index":46,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PYH2DTSNGM7D6MK7SNFEWW6J5M","json":"https://pith.science/pith/PYH2DTSNGM7D6MK7SNFEWW6J5M.json","graph_json":"https://pith.science/api/pith-number/PYH2DTSNGM7D6MK7SNFEWW6J5M/graph.json","events_json":"https://pith.science/api/pith-number/PYH2DTSNGM7D6MK7SNFEWW6J5M/events.json","paper":"https://pith.science/paper/PYH2DTSN"},"agent_actions":{"view_html":"https://pith.science/pith/PYH2DTSNGM7D6MK7SNFEWW6J5M","download_json":"https://pith.science/pith/PYH2DTSNGM7D6MK7SNFEWW6J5M.json","view_paper":"https://pith.science/paper/PYH2DTSN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.08201&json=true","fetch_graph":"https://pith.science/api/pith-number/PYH2DTSNGM7D6MK7SNFEWW6J5M/graph.json","fetch_events":"https://pith.science/api/pith-number/PYH2DTSNGM7D6MK7SNFEWW6J5M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PYH2DTSNGM7D6MK7SNFEWW6J5M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PYH2DTSNGM7D6MK7SNFEWW6J5M/action/storage_attestation","attest_author":"https://pith.science/pith/PYH2DTSNGM7D6MK7SNFEWW6J5M/action/author_attestation","sign_citation":"https://pith.science/pith/PYH2DTSNGM7D6MK7SNFEWW6J5M/action/citation_signature","submit_replication":"https://pith.science/pith/PYH2DTSNGM7D6MK7SNFEWW6J5M/action/replication_record"}},"created_at":"2026-07-05T11:09:11.276233+00:00","updated_at":"2026-07-05T11:09:11.276233+00:00"}