{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:OP6JBWTFSXBOYUSP4ATGQYMSPI","short_pith_number":"pith:OP6JBWTF","canonical_record":{"source":{"id":"2604.04958","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2026-04-03T13:46:41Z","cross_cats_sorted":["cs.AI","q-bio.NC"],"title_canon_sha256":"743659becab8043dcc2f8cd9227d510a4af00ac3f85dcc1443849ac5d9de3a35","abstract_canon_sha256":"10cc471c22c13dc4e5aac9c2166929d863946cd7202548701c030ee454216853"},"schema_version":"1.0"},"canonical_sha256":"73fc90da6595c2ec524fe0266861927a03c8f50ff1c745b8bad5430137ab6530","source":{"kind":"arxiv","id":"2604.04958","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.04958","created_at":"2026-06-02T02:04:52Z"},{"alias_kind":"arxiv_version","alias_value":"2604.04958v3","created_at":"2026-06-02T02:04:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.04958","created_at":"2026-06-02T02:04:52Z"},{"alias_kind":"pith_short_12","alias_value":"OP6JBWTFSXBO","created_at":"2026-06-02T02:04:52Z"},{"alias_kind":"pith_short_16","alias_value":"OP6JBWTFSXBOYUSP","created_at":"2026-06-02T02:04:52Z"},{"alias_kind":"pith_short_8","alias_value":"OP6JBWTF","created_at":"2026-06-02T02:04:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:OP6JBWTFSXBOYUSP4ATGQYMSPI","target":"record","payload":{"canonical_record":{"source":{"id":"2604.04958","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2026-04-03T13:46:41Z","cross_cats_sorted":["cs.AI","q-bio.NC"],"title_canon_sha256":"743659becab8043dcc2f8cd9227d510a4af00ac3f85dcc1443849ac5d9de3a35","abstract_canon_sha256":"10cc471c22c13dc4e5aac9c2166929d863946cd7202548701c030ee454216853"},"schema_version":"1.0"},"canonical_sha256":"73fc90da6595c2ec524fe0266861927a03c8f50ff1c745b8bad5430137ab6530","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:52.785333Z","signature_b64":"IdDMBk1wFdDvlfrvBBT7zFBBEEz7Q0GGFRg+sH5il++tEDx1Y9Hvg92iXjcv0smLJ1nK1i8ZxT4bPkJ/E/E5BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73fc90da6595c2ec524fe0266861927a03c8f50ff1c745b8bad5430137ab6530","last_reissued_at":"2026-06-02T02:04:52.784866Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:52.784866Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.04958","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-02T02:04:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hh0F3hEN7CVOlajrmLMnFqjS8hweb3i/Bxsuf9WJPp5vpVpiazk+KekL15o8hKCwhBVuK9w+ROJ3MH5dr31VDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T09:15:34.958328Z"},"content_sha256":"c3df14465f9b8f253617faa271f3973fbeb5d1f1afbb858480348b0f9dac8dcc","schema_version":"1.0","event_id":"sha256:c3df14465f9b8f253617faa271f3973fbeb5d1f1afbb858480348b0f9dac8dcc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:OP6JBWTFSXBOYUSP4ATGQYMSPI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A self-supervised model pretrained on calcium traces forecasts neural population dynamics better than specialized baselines and adapts to decode behavior.","cross_cats":["cs.AI","q-bio.NC"],"primary_cat":"q-bio.QM","authors_text":"Qichen Qian, Xinhong Xu, Yimeng Zhang, Yuanlong Zhang","submitted_at":"2026-04-03T13:46:41Z","abstract_excerpt":"Recent work suggests that large-scale, multi-animal modeling can significantly improve neural recording analysis. However, for functional calcium traces, existing approaches remain task-specific, limiting transfer across common neuroscience objectives. To address this challenge, we propose \\textbf{CalM}, a self-supervised neural foundation model trained solely on neuronal calcium traces and adaptable to multiple downstream tasks, including forecasting and decoding. Our key contribution is a pretraining framework, composed of a high-performance tokenizer mapping single-neuron traces into a shar"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"On the neural population dynamics forecasting task, CalM outperforms strong specialized baselines after pretraining. With a task-specific head, CalM further adapts to the behavior decoding task and achieves superior results compared with supervised decoding models.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the self-supervised pretraining framework with the proposed tokenizer and dual-axis transformer learns representations that transfer effectively to multiple downstream tasks without requiring extensive task-specific architectural changes or data curation.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"CalM uses a discrete tokenizer and dual-axis autoregressive transformer pretrained self-supervised on calcium traces to outperform specialized baselines on population dynamics forecasting and adapt to superior behavior decoding.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A self-supervised model pretrained on calcium traces forecasts neural population dynamics better than specialized baselines and adapts to decode behavior.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"06b0111031f698f7bd69adba667445ec8c5f048474b14d993d16fc781d8935e6"},"source":{"id":"2604.04958","kind":"arxiv","version":3},"verdict":{"id":"829685fb-b9df-4dfa-9224-1e0896e73a38","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-13T18:27:10.581242Z","strongest_claim":"On the neural population dynamics forecasting task, CalM outperforms strong specialized baselines after pretraining. With a task-specific head, CalM further adapts to the behavior decoding task and achieves superior results compared with supervised decoding models.","one_line_summary":"CalM uses a discrete tokenizer and dual-axis autoregressive transformer pretrained self-supervised on calcium traces to outperform specialized baselines on population dynamics forecasting and adapt to superior behavior decoding.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the self-supervised pretraining framework with the proposed tokenizer and dual-axis transformer learns representations that transfer effectively to multiple downstream tasks without requiring extensive task-specific architectural changes or data curation.","pith_extraction_headline":"A self-supervised model pretrained on calcium traces forecasts neural population dynamics better than specialized baselines and adapts to decode behavior."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.04958/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":2,"snapshot_sha256":"851f205b46f4a06c91b270761801e31eb21f29f9417e12a50029688288b3be6c"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"829685fb-b9df-4dfa-9224-1e0896e73a38"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-02T02:04:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qL2odc3BKR67oYcyf8YsyZC6tmpGTW1wjqYa0e3wWk9lWNbBay8IMSysAhdhfecCw2QNRQLT4mk+v5juAnXjAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T09:15:34.958988Z"},"content_sha256":"5ae2adaa240de681c187f5a7f7ea7ff263d3b04f84aa45c1faed1f70651beb50","schema_version":"1.0","event_id":"sha256:5ae2adaa240de681c187f5a7f7ea7ff263d3b04f84aa45c1faed1f70651beb50"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OP6JBWTFSXBOYUSP4ATGQYMSPI/bundle.json","state_url":"https://pith.science/pith/OP6JBWTFSXBOYUSP4ATGQYMSPI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OP6JBWTFSXBOYUSP4ATGQYMSPI/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":"2026-08-04T09:15:34Z","links":{"resolver":"https://pith.science/pith/OP6JBWTFSXBOYUSP4ATGQYMSPI","bundle":"https://pith.science/pith/OP6JBWTFSXBOYUSP4ATGQYMSPI/bundle.json","state":"https://pith.science/pith/OP6JBWTFSXBOYUSP4ATGQYMSPI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OP6JBWTFSXBOYUSP4ATGQYMSPI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:OP6JBWTFSXBOYUSP4ATGQYMSPI","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":"10cc471c22c13dc4e5aac9c2166929d863946cd7202548701c030ee454216853","cross_cats_sorted":["cs.AI","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2026-04-03T13:46:41Z","title_canon_sha256":"743659becab8043dcc2f8cd9227d510a4af00ac3f85dcc1443849ac5d9de3a35"},"schema_version":"1.0","source":{"id":"2604.04958","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.04958","created_at":"2026-06-02T02:04:52Z"},{"alias_kind":"arxiv_version","alias_value":"2604.04958v3","created_at":"2026-06-02T02:04:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.04958","created_at":"2026-06-02T02:04:52Z"},{"alias_kind":"pith_short_12","alias_value":"OP6JBWTFSXBO","created_at":"2026-06-02T02:04:52Z"},{"alias_kind":"pith_short_16","alias_value":"OP6JBWTFSXBOYUSP","created_at":"2026-06-02T02:04:52Z"},{"alias_kind":"pith_short_8","alias_value":"OP6JBWTF","created_at":"2026-06-02T02:04:52Z"}],"graph_snapshots":[{"event_id":"sha256:5ae2adaa240de681c187f5a7f7ea7ff263d3b04f84aa45c1faed1f70651beb50","target":"graph","created_at":"2026-06-02T02:04:52Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"On the neural population dynamics forecasting task, CalM outperforms strong specialized baselines after pretraining. With a task-specific head, CalM further adapts to the behavior decoding task and achieves superior results compared with supervised decoding models."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the self-supervised pretraining framework with the proposed tokenizer and dual-axis transformer learns representations that transfer effectively to multiple downstream tasks without requiring extensive task-specific architectural changes or data curation."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"CalM uses a discrete tokenizer and dual-axis autoregressive transformer pretrained self-supervised on calcium traces to outperform specialized baselines on population dynamics forecasting and adapt to superior behavior decoding."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A self-supervised model pretrained on calcium traces forecasts neural population dynamics better than specialized baselines and adapts to decode behavior."}],"snapshot_sha256":"06b0111031f698f7bd69adba667445ec8c5f048474b14d993d16fc781d8935e6"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"851f205b46f4a06c91b270761801e31eb21f29f9417e12a50029688288b3be6c"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.04958/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent work suggests that large-scale, multi-animal modeling can significantly improve neural recording analysis. However, for functional calcium traces, existing approaches remain task-specific, limiting transfer across common neuroscience objectives. To address this challenge, we propose \\textbf{CalM}, a self-supervised neural foundation model trained solely on neuronal calcium traces and adaptable to multiple downstream tasks, including forecasting and decoding. Our key contribution is a pretraining framework, composed of a high-performance tokenizer mapping single-neuron traces into a shar","authors_text":"Qichen Qian, Xinhong Xu, Yimeng Zhang, Yuanlong Zhang","cross_cats":["cs.AI","q-bio.NC"],"headline":"A self-supervised model pretrained on calcium traces forecasts neural population dynamics better than specialized baselines and adapts to decode behavior.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2026-04-03T13:46:41Z","title":"CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.04958","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-13T18:27:10.581242Z","id":"829685fb-b9df-4dfa-9224-1e0896e73a38","model_set":{"reader":"grok-4.3"},"one_line_summary":"CalM uses a discrete tokenizer and dual-axis autoregressive transformer pretrained self-supervised on calcium traces to outperform specialized baselines on population dynamics forecasting and adapt to superior behavior decoding.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A self-supervised model pretrained on calcium traces forecasts neural population dynamics better than specialized baselines and adapts to decode behavior.","strongest_claim":"On the neural population dynamics forecasting task, CalM outperforms strong specialized baselines after pretraining. With a task-specific head, CalM further adapts to the behavior decoding task and achieves superior results compared with supervised decoding models.","weakest_assumption":"That the self-supervised pretraining framework with the proposed tokenizer and dual-axis transformer learns representations that transfer effectively to multiple downstream tasks without requiring extensive task-specific architectural changes or data curation."}},"verdict_id":"829685fb-b9df-4dfa-9224-1e0896e73a38"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c3df14465f9b8f253617faa271f3973fbeb5d1f1afbb858480348b0f9dac8dcc","target":"record","created_at":"2026-06-02T02:04:52Z","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":"10cc471c22c13dc4e5aac9c2166929d863946cd7202548701c030ee454216853","cross_cats_sorted":["cs.AI","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2026-04-03T13:46:41Z","title_canon_sha256":"743659becab8043dcc2f8cd9227d510a4af00ac3f85dcc1443849ac5d9de3a35"},"schema_version":"1.0","source":{"id":"2604.04958","kind":"arxiv","version":3}},"canonical_sha256":"73fc90da6595c2ec524fe0266861927a03c8f50ff1c745b8bad5430137ab6530","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73fc90da6595c2ec524fe0266861927a03c8f50ff1c745b8bad5430137ab6530","first_computed_at":"2026-06-02T02:04:52.784866Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T02:04:52.784866Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IdDMBk1wFdDvlfrvBBT7zFBBEEz7Q0GGFRg+sH5il++tEDx1Y9Hvg92iXjcv0smLJ1nK1i8ZxT4bPkJ/E/E5BA==","signature_status":"signed_v1","signed_at":"2026-06-02T02:04:52.785333Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.04958","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c3df14465f9b8f253617faa271f3973fbeb5d1f1afbb858480348b0f9dac8dcc","sha256:5ae2adaa240de681c187f5a7f7ea7ff263d3b04f84aa45c1faed1f70651beb50"],"state_sha256":"401e7067e41ffe1e64fe05992fea7b35816a07fb91fad4604db682d82a4855db"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SUHrAZU6NrpfvlVL588nOvI67bVgxY7p2yoj7WlAI7A7+Lj/TF+xgoLxscDVd6fuw+u/8bFLKlI1ppXxNv4QCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T09:15:34.963197Z","bundle_sha256":"40aa6456a5b2ee7bc1347b0d545c7a8f92c142a8c70660e18e80ba3e81c50af4"}}