Paper Citation Record · LEDGER

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection

As of 21 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2508.20392.

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

pith.paper-citation-record.v1
2508.20392 v2

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:53:22.093537Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

18 of 18 outbound references displayed

verified exact0
verified fuzzy8
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4df34e73-3100-4434-941b-7802e222d267 · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T16:53:21.741999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 508b2fbf-baba-4012-80d9-6ddd9fdecd76 · outbound

This paper cites U., Neil, D., Binas, J., Cook, M., Liu, S.-C., and Pfeiffer, M.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection U., Neil, D., Binas, J., Cook, M., Liu, S.-C., and Pfeiffer, M

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T16:53:21.899553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b32490c1-821f-44e9-829b-0eae0834e30b · outbound

This paper cites Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T16:53:21.903458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ade2730-5d7c-4f2e-a156-8c30b4fbbd72 · outbound

This paper cites Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentation.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T16:53:21.917371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 295d5a6a-08c3-4365-9647-7d45ec2bc438 · outbound

This paper cites an unresolved cited work.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T16:53:21.922253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c1d40b7-b8da-46df-b6e3-1ee99c702085 · outbound

This paper cites Open the box of digital neuromor- phic processor: Towards effective algorithm-hardware co-design.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection Open the box of digital neuromor- phic processor: Towards effective algorithm-hardware co-design

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:53:22.236232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9ef06463-e059-40e2-b835-91fcf59a41e2 · outbound

This paper cites YOLOX: Exceeding YOLO Series in 2021.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection YOLOX: Exceeding YOLO Series in 2021

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T16:53:22.084479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1226da1a-818c-4f2e-9006-534741db51af · outbound

This paper cites CULane(Pan et al., 2018).

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection CULane(Pan et al., 2018)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:53:22.222832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 09222a65-7848-4d51-8d4c-38bb8363c81f · outbound

This paper cites IoU between the predicted lane line and GT label is taken for judging whether a sample is true positive (TP) or false positive (FP) or false negative (FN).

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection IoU between the predicted lane line and GT label is taken for judging whether a sample is true positive (TP) or false positive (FP) or false negative (FN)

Reference 128

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:53:22.210407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 29e1eacf-0cda-43ed-992b-2f84c64e8ceb · outbound

This paper cites V ., Dittmann, R., Linares-Barranco, B., Sebastian, A., Le Gallo, M., Redaelli, A., Slesazeck, S., Mikolajick, T., Spiga, S., Menzel, S., et al.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection V ., Dittmann, R., Linares-Barranco, B., Sebastian, A., Le Gallo, M., Redaelli, A., Slesazeck, S., Mikolajick, T., Spiga, S., Menzel, S., et al

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:53:22.626526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 88c9667f-479c-427a-8bd1-c59e0b13528f · outbound

This paper cites Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-15T16:53:21.912828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa4c5523-de1e-4d5c-bb97-64fd23a430ad · outbound

This paper cites Lane detection and classification using cascaded cnns.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection Lane detection and classification using cascaded cnns

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:53:22.434077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b00dd986-fb79-4553-bddb-0c0893984efc · outbound

This paper cites A., Garside, J., Temple, S., Galluppi, F., Patterson, C., Lester, D.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection A., Garside, J., Temple, S., Galluppi, F., Patterson, C., Lester, D

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:53:22.521395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 412f9ac5-39e0-4960-895c-be5825218a11 · outbound

This paper cites Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T16:53:21.880633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4fcfe2d-da61-4cb2-8288-d33fc3e29eed · outbound

This paper cites Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T16:53:21.894768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4f590b9-dcd4-4afa-b13a-49030445782c · outbound

This paper cites Object detec- tion with spiking neural networks on automotive event data.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection Object detec- tion with spiking neural networks on automotive event data

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:53:22.578181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ae41fe7-d9ac-49b7-aeac-8d2a7e270a2d · outbound

This paper cites an unresolved cited work.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection Unresolved cited work

Reference 2023

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:53:22.557751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ef483a46-0e1b-4f82-8c10-123bf4005019 · outbound

This paper cites Optimal ann-snn conversion with group neurons.

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection Optimal ann-snn conversion with group neurons

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:53:22.535768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.