Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2206.06178.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:36:35.815064Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T13:48:21.586183Z
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 09530f1b-91b3-4ca0-8c3f-3d97c1d4d5b5 · inbound
Spike-TBR: a Noise Resilient Neuromorphic Event Representation cites this paper.
Spike-TBR: a Noise Resilient Neuromorphic Event Representation Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:36:35.815064Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2383e970-80c5-4dde-b56d-b481b5d7b017 · inbound
EEvAct: Early Event-Based Action Recognition with High-Rate Two-Stream Spiking Neural Networks cites this paper.
EEvAct: Early Event-Based Action Recognition with High-Rate Two-Stream Spiking Neural Networks Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:38:18.427735Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4b6fdb0e-78f1-4cc9-8b68-3f150ec400cf · inbound
LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning cites this paper.
LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
Reference 94
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T13:48:21.587691Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.