Energy-efficient traffic sign recognition using directly trained spiking neural networks and population decoding.

Schulte JV, Peters S

Open source

DOI: 10.3389/fnins.2026.1771436
Published: 2026
Container: Frontiers in neuroscience
Publisher: Not recorded
Open access: yes

Credibility signals

uncertain Score 53/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
cautionDOI resolves: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
supportingDirectory of Open Access Journals: A matching record was returned by DOAJ. Source: DOAJ; license: CC0
not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredROR affiliation: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

Cite this work

BibTeX

@article{allodium:10.3389/fnins.2026.1771436,
  title = {Energy-efficient traffic sign recognition using directly trained spiking neural networks and population decoding.},
  author = {Schulte JV and Peters S},
  year = {2026},
  journal = {Frontiers in neuroscience},
  doi = {10.3389/fnins.2026.1771436},
  url = {https://doi.org/10.3389/fnins.2026.1771436}
}

RIS

TY  - JOUR
TI  - Energy-efficient traffic sign recognition using directly trained spiking neural networks and population decoding.
AU  - Schulte JV
AU  - Peters S
PY  - 2026
JO  - Frontiers in neuroscience
DO  - 10.3389/fnins.2026.1771436
UR  - https://doi.org/10.3389/fnins.2026.1771436
ER  -

APA

JV, S., & S, P. (2026). Energy-efficient traffic sign recognition using directly trained spiking neural networks and population decoding.. Frontiers in neuroscience. https://doi.org/10.3389/fnins.2026.1771436

Source records

pubmed · retrieved 2026-09-27T07:33:20.518Z
europe-pmc · retrieved 2026-09-27T07:33:20.527Z
doaj · retrieved 2026-09-27T07:33:20.537Z