Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2308.10373.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:01:34.244901Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T21:45:00.682192Z
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 3d95507d-b44f-4bd8-b7e1-c5d60cc7c366 · inbound
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection cites this paper.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:01:34.244901Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3de941ac-642b-4013-863a-28584a6c80b2 · inbound
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods cites this paper.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:45:00.739632Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.