Pith Number

pith:F43YNSQH

pith:2026:F43YNSQHWZUMSYSIDE4NLWOYZK

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NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework

Alessandro Savino, Alessio Caviglia, Filippo Marostica, Roberta Bardini, Stefano Di Carlo

A single taxonomy sorts spiking neural network training methods by their signals and locality while a shared code base lets researchers test them together.

arxiv:2605.15058 v1 · 2026-05-14 · cs.NE · cs.AI

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Claims

C1strongest claim

The survey provides a comprehensive taxonomy of SNN training algorithms spanning surrogate-gradient backpropagation, local and three-factor learning rules, biologically inspired plasticity, ANN-to-SNN conversion, and non-standard optimization, supported by the release of NeuroTrain for consistent benchmarking.

C2weakest assumption

That the representative algorithms implemented in NeuroTrain sufficiently capture the diversity and key properties of the broader literature without significant omissions or implementation biases.

C3one line summary

A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.

References

191 extracted · 191 resolved · 1 Pith anchors

[1] Maass, Networks of spiking neurons: The third generation of neural network models, Neural Net- works 10 (9) (1997) 1659–1671.doi:10.1016/ S0893-6080(97)00011-7 1997

[2] M. Davies, N. Srinivasa, T.-H. Lin, G. Chinya, Y . Cao, S. H. Choday, G. Dimou, P. Joshi, N. Imam, S. Jain, Y . Liao, C.-K. Lin, A. Lines, R. Liu, D. Mathaikutty, S. McCoy, A. Paul, J. Tse, G. Venkata 2018 · doi:10.1109/mm.2018

[3] B. Huo, F. Li, S. Peng, H. Chen, S. Xin, H. Wang, Research on SNN Learning Algorithms and Networks Based on Biological Plausibility, IEEE Access 13 (2025) 95243–95256.doi:10.1109/ACCESS.2025.3566717 2025 · doi:10.1109/access.2025.3566717

[4] P. Lansky, S. Ditlevsen, A review of the methods for signal estimation in stochastic diffusion leaky integrate- and-fire neuronal models, Biological cybernetics 99 (4) (2008) 253–262.doi:10.1007/s0042 2008 · doi:10.1007/s00422-008-0237-x

[6] B. Mészáros, J. C. Knight, T. Nowotny, Efficient event- based delay learning in spiking neural networks, Nature Communications 16 (1) (2025) 10422.doi:10.1038/ s41467-025-65394-8 2025

Formal links

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Receipt and verification

First computed	2026-05-17T23:38:54.318833Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2f3786ca07b668c962481938d5d9d8cabcf10274409c095b96686dd5bc8eeb93

Aliases

arxiv: 2605.15058 · arxiv_version: 2605.15058v1 · doi: 10.48550/arxiv.2605.15058 · pith_short_12: F43YNSQHWZUM · pith_short_16: F43YNSQHWZUMSYSI · pith_short_8: F43YNSQH

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/F43YNSQHWZUMSYSIDE4NLWOYZK \
  | 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: 2f3786ca07b668c962481938d5d9d8cabcf10274409c095b96686dd5bc8eeb93

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "1d44b4d5504e5fdf8d099d21cfa6a6bcc968772ebe8b7ac604585502ddc7023f",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.NE",
    "submitted_at": "2026-05-14T16:50:15Z",
    "title_canon_sha256": "64d938c5f029bce8af36d31103e8e1ee7290d1b8ee270e0d34d6e08f8a1d1805"
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  "source": {
    "id": "2605.15058",
    "kind": "arxiv",
    "version": 1
  }
}