Pith Number

pith:RXK7LJGH

pith:2026:RXK7LJGHY5CTKWN3NV6PVFPS5L

not attested not anchored not stored refs resolved

Implicit Behavioral Decoding from Next-Step Spike Forecasts at Population Scale

Ash Robbins, David Haussler, Jason Eshraghian, Jesus Gonzalez-Ferrer, Jinghui Geng, John R. Minnick, Kamran Hussain, Mircea Teodorescu, Mohammed A. Mostajo-Radji

A single Mamba forecaster trained only on next-step spike counts delivers both neural forecasts and improved behavioral decoding in one forward pass.

arxiv:2605.12999 v1 · 2026-05-13 · q-bio.NC · cs.LG

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Claims

C1strongest claim

A single Mamba forecaster, trained only on next-step spike counts at Neuropixels scale, can deliver both a forecast of upcoming neural population activity and a readout of the animal's behavioral state in one forward pass. Mamba's predicted rates decode mouse choice at 75.7±0.2% trial vote and stimulus side at 66.1±0.6%, outperforming a matched linear decoder on raw spikes by 4-6 pp.

C2weakest assumption

That the performance gain arises specifically from the forecasting objective and learned representations rather than from differences in temporal context handling, model capacity, or post-hoc per-session fitting of the linear head.

C3one line summary

Mamba forecaster trained on next-step spikes decodes mouse choice at 75.7% and stimulus at 66.1%, beating linear decoding on raw spikes by 4-6 percentage points.

References

13 extracted · 13 resolved · 1 Pith anchors

[1] M. Azabou, V. Arora, V. Ganesh, X. Mao, S. Nachimuthu, M. Mendelson, B. Richards, M. Perich, G. Lajoie, and E. Dyer. A Unified, Scalable Framework for Neural Population Decoding. In NeurIPS, 2023 2023

[2] L. Duncker and M. Sahani. Temporal alignment and latent Gaussian process factor inference in population spike trains. NeurIPS, 2018 2018

[3] Mamba: Linear-Time Sequence Modeling with Selective State Spaces 2023 · arXiv:2312.00752

[4] International Brain Laboratory, K. Banga, J. Benson, J. Bhagat, D. Biderman, D. Birman, N. Bonacchi, S. A. Bruijns, K. Buchanan, R. A. A. Campbell, et al. Reproducibility of in vivo electrophysiologic 2025

[5] S. W. Linderman, M. J. Johnson, A. C. Miller, R. P. Adams, D. M. Blei, and L. Paninski. Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. In AISTATS, volume 54, pages 91 2017

Receipt and verification

First computed	2026-05-18T03:09:00.458029Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8dd5f5a4c7c7453559bb6d7cfa95f2eada7f707eb24a921142d5aecc75434d9a

Aliases

arxiv: 2605.12999 · arxiv_version: 2605.12999v1 · doi: 10.48550/arxiv.2605.12999 · pith_short_12: RXK7LJGHY5CT · pith_short_16: RXK7LJGHY5CTKWN3 · pith_short_8: RXK7LJGH

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/RXK7LJGHY5CTKWN3NV6PVFPS5L \
  | 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: 8dd5f5a4c7c7453559bb6d7cfa95f2eada7f707eb24a921142d5aecc75434d9a

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "007272008734eb7fde2955edf9e9acab4d5bbbb50e741a3220d75cb6fb0d4ec7",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "q-bio.NC",
    "submitted_at": "2026-05-13T04:55:03Z",
    "title_canon_sha256": "5788eb664e6f5a7e361904febfa65259919bf8445cd2ec8c2fdae61d6ce3fcc0"
  },
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  "source": {
    "id": "2605.12999",
    "kind": "arxiv",
    "version": 1
  }
}