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Paper Citation Record · LEDGER

Attention Spiking Neural Networks

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2209.13929.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2209.13929 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:23:09.316585Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-12T12:39:37.637038Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c1421006-1101-45d2-ae27-5bea5083ebe0 · inbound

Event-based Spiking Neural Networks for Object Detection: A Review of Datasets, Architectures, Learning Rules, and Implementation cites this paper.

Event-based Spiking Neural Networks for Object Detection: A Review of Datasets, Architectures, Learning Rules, and Implementation Attention Spiking Neural Networks

Reference 228

Resolution
verified exact
local_arxiv, observed 2026-08-12T12:39:37.644533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T12:39:36.923089Z digest=sha256:b3eef0f0caee4275103dbef69426605bc63bba7380326d985daa079ab61cd3f0

Observation 1888c2df-9c17-44b5-a80b-ba4407615277 · inbound

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion cites this paper.

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion Attention Spiking Neural Networks

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-15T20:23:09.316585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:23:09.316585Z digest=sha256:4a47806ba46824c4c8b6b89d8e97038dea1eaad6229a03c4ca7c0ef3ac9e84b3