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pith:OP6JBWTF

pith:2026:OP6JBWTFSXBOYUSP4ATGQYMSPI
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CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data

Qichen Qian, Xinhong Xu, Yimeng Zhang, Yuanlong Zhang

A self-supervised model pretrained on calcium traces forecasts neural population dynamics better than specialized baselines and adapts to decode behavior.

arxiv:2604.04958 v3 · 2026-04-03 · q-bio.QM · cs.AI · q-bio.NC

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\pithnumber{OP6JBWTFSXBOYUSP4ATGQYMSPI}

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Record completeness

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2 Internet Archive
3 Author claim open · sign in to claim
4 Citations open
5 Replications open
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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest 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.

C2weakest 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.

C3one 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.

Formal links

2 machine-checked theorem links

Receipt and verification
First computed 2026-06-02T02:04:52.784866Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

73fc90da6595c2ec524fe0266861927a03c8f50ff1c745b8bad5430137ab6530

Aliases

arxiv: 2604.04958 · arxiv_version: 2604.04958v3 · doi: 10.48550/arxiv.2604.04958 · pith_short_12: OP6JBWTFSXBO · pith_short_16: OP6JBWTFSXBOYUSP · pith_short_8: OP6JBWTF
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/OP6JBWTFSXBOYUSP4ATGQYMSPI \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 73fc90da6595c2ec524fe0266861927a03c8f50ff1c745b8bad5430137ab6530
Canonical record JSON
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    "abstract_canon_sha256": "10cc471c22c13dc4e5aac9c2166929d863946cd7202548701c030ee454216853",
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      "cs.AI",
      "q-bio.NC"
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    "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"
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    "kind": "arxiv",
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