Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1612.04052.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T12:39:35.911636Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T10:21:54.062845Z
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 fff4cf55-2430-4f10-85a3-ea3237d9c2d6 · 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 Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T12:39:35.911636Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ea12d302-bd19-4d56-851e-0f4634850c77 · inbound
FAS: Fast ANN-SNN Conversion for Spiking Large Language Models cites this paper.
FAS: Fast ANN-SNN Conversion for Spiking Large Language Models Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T00:24:58.318291Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d074ed90-3837-4e82-a7b1-22d1d35f03a7 · inbound
Integer Binary-Range Alignment Neuron for Spiking Neural Networks cites this paper.
Integer Binary-Range Alignment Neuron for Spiking Neural Networks Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T10:21:54.066988Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.