Pith Number

pith:OGHAE6UY

pith:2026:OGHAE6UYPBOV6JU5J7OEEEZG46

not attested not anchored not stored refs resolved

SpikeProphecy: A Large-Scale Benchmark for Autoregressive Neural Population Forecasting

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

A three-part breakdown of spike forecasting metrics reveals stable brain-region predictability rankings that hold after correcting for firing statistics.

arxiv:2605.12992 v1 · 2026-05-13 · q-bio.NC · cs.LG

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\usepackage{pith}
\pithnumber{OGHAE6UYPBOV6JU5J7OEEEZG46}

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1 Bitcoin timestamp

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

The population metric decomposition surfaces a brain-region predictability ranking that reproduces across all seven baselines and survives ANCOVA correction for firing-statistics constraints (region ΔR² = 0.018 above the firing-statistics covariates). It also exposes a sub-Poisson evaluation floor and yields a negative result on KL-on-output-rates distillation for ANN-to-SNN transfer.

C2weakest assumption

That the three-way metric decomposition captures biologically meaningful and independent aspects of forecasting quality rather than merely re-expressing the same aggregate correlation in different coordinates, and that the ANCOVA covariates fully capture firing-statistics confounds without residual selection effects from the chosen sessions.

C3one line summary

SpikeProphecy decomposes spike-count forecasting performance into temporal fidelity, spatial pattern accuracy, and magnitude-invariant alignment, revealing reproducible brain-region predictability rankings and a sub-Poisson evaluation floor across seven model families on 105 Neuropixels sessions.

References

17 extracted · 17 resolved · 4 Pith anchors

[1] Advances in Neural Information Processing Systems , volume=

[2] POCO: Scalable neural forecasting through population conditioning , author=. ArXiv , pages=

[3] Neuronal population coding of movement direction , author=. Science , volume=. 1986 , publisher= 1986

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

[5] arXiv preprint arXiv:2404.07904 , year=

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:09:00.524583Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

718e027a98785d5f269d4fdc421326e788184cd5c40f069bc8b0098fb0c0294e

Aliases

arxiv: 2605.12992 · arxiv_version: 2605.12992v1 · doi: 10.48550/arxiv.2605.12992 · pith_short_12: OGHAE6UYPBOV · pith_short_16: OGHAE6UYPBOV6JU5 · pith_short_8: OGHAE6UY

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/OGHAE6UYPBOV6JU5J7OEEEZG46 \
  | 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: 718e027a98785d5f269d4fdc421326e788184cd5c40f069bc8b0098fb0c0294e

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "25419648de73807054565c42ffa419fdb30a5f85a19e7744ec39d36650b73113",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "q-bio.NC",
    "submitted_at": "2026-05-13T04:45:35Z",
    "title_canon_sha256": "c90e7dd64600257aed60ce0537a6d9898bbd84a989f9948b5c7494e159a0bd28"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12992",
    "kind": "arxiv",
    "version": 1
  }
}