Paper Citation Record · LEDGER

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks

As of 13 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 2 inbound Pith citation observations for arXiv:2411.16711.

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

pith.paper-citation-record.v1
2411.16711 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:37:56.120149Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-23T00:33:23.378132Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T00:35:15.141932Z

Reference resolution

60 of 60 outbound references displayed

verified exact0
verified fuzzy46
unresolved14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e6adb169-83bc-4483-9f7c-bb281495bc7c · outbound

This paper cites write newline.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks write newline

Reference 1

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Source-reported events for the cited work

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Observation 784f5ca4-2bcc-4921-8c31-b6d442c7f2f5 · outbound

This paper cites Lapicque’s introduction of the integrate-and-fire model neuron (1907).

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Lapicque’s introduction of the integrate-and-fire model neuron (1907)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:57.091923Z

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Observation a3106b5c-2c95-48a2-83bb-3e24829be87a · outbound

This paper cites Skip connections in spiking neural networks: An analysis of their effect on network training, 2023.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Skip connections in spiking neural networks: An analysis of their effect on network training, 2023

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:57.075412Z

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Observation adcff4e7-ce23-4990-8123-66726c21970e · outbound

This paper cites A surrogate gradient spiking baseline for speech command recognition.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks A surrogate gradient spiking baseline for speech command recognition

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:57.058614Z

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Observation bbb80603-9770-42ea-b952-22ea82a427af · outbound

This paper cites A 240 180 130 db 3 s latency global shutter spatiotemporal vision sensor.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks A 240 180 130 db 3 s latency global shutter spatiotemporal vision sensor

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:57.042106Z

Source-reported events for the cited work

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Observation dfb8a87b-8768-4a3f-865e-bc0c9c65079a · outbound

This paper cites Once-for-all: Train one network and specialize it for efficient deployment, 2020.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Once-for-all: Train one network and specialize it for efficient deployment, 2020

Reference 6

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Source-reported events for the cited work

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Observation a835df82-3165-41f1-aa3d-caf95cf6746a · outbound

This paper cites Spiking deep convolutional neural networks for energy-efficient object recognition.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Spiking deep convolutional neural networks for energy-efficient object recognition

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:57.014730Z

Source-reported events for the cited work

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Observation 8e12d61b-10bb-4114-8bfa-0d5af3a43b60 · outbound

This paper cites One timestep is all you need: Training spiking neural networks with ultra low latency, 2021.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks One timestep is all you need: Training spiking neural networks with ultra low latency, 2021

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.998264Z

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Observation a4f967ea-929c-41c2-9f1f-dc1ba001d74b · outbound

This paper cites The heidelberg spiking data sets for the systematic evaluation of spiking neural networks.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks The heidelberg spiking data sets for the systematic evaluation of spiking neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.981262Z

Source-reported events for the cited work

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Observation e6ff9778-9391-4253-bcb1-1d015baedefe · outbound

This paper cites Investigating current-based and gating approaches for accurate and energy-efficient spiking recurrent neural networks.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Investigating current-based and gating approaches for accurate and energy-efficient spiking recurrent neural networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.964434Z

Source-reported events for the cited work

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Observation e09df304-404f-4ae9-9708-7ad29c3951c2 · outbound

This paper cites Rethinking the performance comparison between snns and anns.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Rethinking the performance comparison between snns and anns

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.947667Z

Source-reported events for the cited work

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Observation 08a2594a-631a-4382-8b5d-c0b892ce22c9 · outbound

This paper cites Incorporating learnable membrane time constant to enhance learning of spiking neural networks.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Incorporating learnable membrane time constant to enhance learning of spiking neural networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.929480Z

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Observation 1379b9ea-98d5-434c-9a9e-65b920336a98 · outbound

This paper cites Davison, Jörg Conradt, Kostas Daniilidis, and Davide Scaramuzza.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Davison, Jörg Conradt, Kostas Daniilidis, and Davide Scaramuzza

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.913722Z

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Observation 67a87037-3e84-4cf0-8087-12fad7d56331 · outbound

This paper cites Dsec: A stereo event camera dataset for driving scenarios.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Dsec: A stereo event camera dataset for driving scenarios

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.898093Z

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Observation 1c0708d7-b5f9-4045-aa1c-a9896fc8e31b · outbound

This paper cites E-raft: Dense optical flow from event cameras.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks E-raft: Dense optical flow from event cameras

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.882217Z

Source-reported events for the cited work

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Observation 43089f8c-b83f-46a9-8755-cbdfff6eeafe · outbound

This paper cites Self-supervised learning of event-based optical flow with spiking neural networks, 2021.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Self-supervised learning of event-based optical flow with spiking neural networks, 2021

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.866096Z

Source-reported events for the cited work

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Observation d2566e19-8632-4c3c-a26f-0309ef831404 · outbound

This paper cites Learning delays in spiking neural networks using dilated convolutions with learnable spacings, 2023.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Learning delays in spiking neural networks using dilated convolutions with learnable spacings, 2023

Reference 17

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Observation 88a44a34-b27c-4bbe-957b-96365b3c8aee · outbound

This paper cites Deep residual learning for image recognition, 2015.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Deep residual learning for image recognition, 2015

Reference 18

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Observation ba5bf94e-70d8-4836-a0e4-dad48a8e856a · outbound

This paper cites Deep residual learning for image recognition.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Deep residual learning for image recognition

Reference 19

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Observation b3db9e04-dcad-4771-89b9-26f1da6e6232 · outbound

This paper cites Long short-term memory.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Long short-term memory

Reference 20

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Observation 08b2b204-e8b3-4da0-83a5-10a87b54efcf · outbound

This paper cites 1.1 computing's energy problem (and what we can do about it).

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks 1.1 computing's energy problem (and what we can do about it)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.804762Z

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Observation b25cc326-89aa-4be4-a06f-220db573b951 · outbound

This paper cites Hand gesture recognition system using the dynamic vision sensor.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Hand gesture recognition system using the dynamic vision sensor

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.787846Z

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.

Observation 17a41b4e-a0f8-4a77-9080-69f5dba66d36 · outbound

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TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Densely connected convolutional networks

Reference 23

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Observation 72e6216e-a117-45be-a932-62a3c8b65a11 · outbound

This paper cites Klif: An optimized spiking neuron unit for tuning surrogate gradient function.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Klif: An optimized spiking neuron unit for tuning surrogate gradient function

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.759068Z

Source-reported events for the cited work

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Observation 58e2c327-3097-44f4-8058-ebf7ead35c3f · outbound

This paper cites Synaptic plasticity dynamics for deep continuous local learning (decolle).

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Synaptic plasticity dynamics for deep continuous local learning (decolle)

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.743035Z

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Observation 35b731d4-0b85-4543-8895-ab2312683726 · outbound

This paper cites Dilated convolution with learnable spacings, 2023.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Dilated convolution with learnable spacings, 2023

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.725120Z

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Observation 5c5e2745-bc32-4ea0-91c8-c20a1597500d · outbound

This paper cites Revisiting batch normalization for training low-latency deep spiking neural networks from scratch.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Revisiting batch normalization for training low-latency deep spiking neural networks from scratch

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.708091Z

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Observation 7fa32e6b-d47a-4a51-a6f2-9e5486138fd7 · outbound

This paper cites Neural architecture search for spiking neural networks, 2022.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Neural architecture search for spiking neural networks, 2022

Reference 28

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Observation 193ae67d-194d-430e-a3fa-6d1c692006e7 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Adam: A Method for Stochastic Optimization

Reference 29

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Observation 06bced28-8eb4-4b71-96fd-4a969aaff768 · outbound

This paper cites Adaptive-spikenet: Event-based optical flow estimation using spiking neural networks with learnable neuronal dynamics, 2023.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Adaptive-spikenet: Event-based optical flow estimation using spiking neural networks with learnable neuronal dynamics, 2023

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.672601Z

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Observation c731a760-78ac-40ef-9af1-c33b5d6393f7 · outbound

This paper cites Fusion-flownet: Energy-efficient optical flow estimation using sensor fusion and deep fused spiking-analog network architectures, 2021.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Fusion-flownet: Energy-efficient optical flow estimation using sensor fusion and deep fused spiking-analog network architectures, 2021

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.655462Z

Source-reported events for the cited work

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Observation 4aeb44b5-0fa4-4e33-9d4d-f84e9c9611c6 · outbound

This paper cites Spikeformer: A novel architecture for training high-performance low-latency spiking neural network, 2022.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Spikeformer: A novel architecture for training high-performance low-latency spiking neural network, 2022

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.637800Z

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Observation 544a1c27-d140-4e81-a981-90a34b1ad4b9 · outbound

This paper cites Blinkflow: A dataset to push the limits of event-based optical flow estimation.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Blinkflow: A dataset to push the limits of event-based optical flow estimation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.622571Z

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Observation cbccf2da-7d6e-463b-ba3e-43a2947bacad · outbound

This paper cites A 128 128 120 db 15 s latency asynchronous temporal contrast vision sensor.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks A 128 128 120 db 15 s latency asynchronous temporal contrast vision sensor

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.606052Z

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Observation 0e36e563-90cc-41c7-b9b1-4fe52e37726a · outbound

This paper cites Sgdr: Stochastic gradient descent with warm restarts, 2017.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Sgdr: Stochastic gradient descent with warm restarts, 2017

Reference 35

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Observation 43470177-ed1a-4261-8a53-8b8cbe8d2d9d · outbound

This paper cites Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks

Reference 36

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Observation aad2dd74-b156-4e71-8615-ea220256eab1 · outbound

This paper cites Best of both worlds: Hybrid snn-ann architecture for event-based optical flow estimation, 2024.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Best of both worlds: Hybrid snn-ann architecture for event-based optical flow estimation, 2024

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.565203Z

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Observation f9dcc2d5-74cb-4ee4-a5d1-5a9a24ea6774 · outbound

This paper cites Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.548286Z

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Observation c241b5f2-7d69-4a5d-a2da-168627884f4b · outbound

This paper cites Back to event basics: Self-supervised learning of image reconstruction for event cameras via photometric constancy.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Back to event basics: Self-supervised learning of image reconstruction for event cameras via photometric constancy

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.529584Z

Source-reported events for the cited work

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Observation 8c25dc63-69fa-4be5-8028-e15a134e50f8 · outbound

This paper cites On the difficulty of training recurrent neural networks, 2013.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks On the difficulty of training recurrent neural networks, 2013

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.511271Z

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Observation 66ecfc20-2ba7-41aa-aef4-8d571e48e507 · outbound

This paper cites Hybrid analog-spiking long short-term memory for energy efficient computing on edge devices.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Hybrid analog-spiking long short-term memory for energy efficient computing on edge devices

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.492771Z

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Observation 2c055e6b-ae76-4fd1-9efb-b5d85bbf79f7 · outbound

This paper cites Event-based temporally dense optical flow estimation with sequential learning, 2023.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Event-based temporally dense optical flow estimation with sequential learning, 2023

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.474276Z

Source-reported events for the cited work

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Observation 45217fb6-9d19-4855-a2ac-bbcbb1c31cd2 · outbound

This paper cites Mamba-spike: Enhancing the mamba architecture with a spiking front-end for efficient temporal data processing, 2024.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Mamba-spike: Enhancing the mamba architecture with a spiking front-end for efficient temporal data processing, 2024

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.456448Z

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Observation 0d9e8901-ba47-4f91-9a45-658a2a443253 · outbound

This paper cites Diet-snn: Direct input encoding with leakage and threshold optimization in deep spiking neural networks, 2020.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Diet-snn: Direct input encoding with leakage and threshold optimization in deep spiking neural networks, 2020

Reference 44

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Observation 2404fa3c-39e9-451c-9ae7-cab59a5a48f4 · outbound

This paper cites Lite-snn: Leveraging inherent dynamics to train energy-efficient spiking neural networks for sequential learning.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Lite-snn: Leveraging inherent dynamics to train energy-efficient spiking neural networks for sequential learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.428200Z

Source-reported events for the cited work

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Observation b0be7aff-0b52-488e-b601-18cece1fa3be · outbound

This paper cites Exploring spike-based learning for neuromorphic computing: Prospects and perspectives.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Exploring spike-based learning for neuromorphic computing: Prospects and perspectives

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.411811Z

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.

Observation bf5fd06b-3712-42c1-9bf9-57fcd7712b02 · outbound

This paper cites Conversion of continuous-valued deep networks to efficient event-driven networks for image classification.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Conversion of continuous-valued deep networks to efficient event-driven networks for image classification

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.396642Z

Source-reported events for the cited work

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Observation 9b4b0e5a-39c8-415f-93f0-2dface43de21 · outbound

This paper cites Learning internal representations by error propagation, parallel distributed processing, explorations in the microstructure of cognition, ed.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Learning internal representations by error propagation, parallel distributed processing, explorations in the microstructure of cognition, ed

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T14:37:56.062751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8602756b-ad63-44cf-bb44-385fcfb0c01b · outbound

This paper cites Convolutional spiking neural networks for spatio-temporal feature extraction.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Convolutional spiking neural networks for spatio-temporal feature extraction

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.370251Z

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.

Observation 00233c81-31bf-4a52-9c2b-6eb5348a1c6d · outbound

This paper cites Eventmix: An efficient data augmentation strategy for event-based learning.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Eventmix: An efficient data augmentation strategy for event-based learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.353376Z

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.

Observation 2d54347b-2775-4790-be70-d39856e60916 · outbound

This paper cites Slayer: Spike layer error reassignment in time, 2018.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Slayer: Spike layer error reassignment in time, 2018

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.333653Z

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.

Observation b158d946-09c4-4500-802c-c1ba3f054fd3 · outbound

This paper cites Learnable axonal delay in spiking neural networks improves spoken word recognition.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Learnable axonal delay in spiking neural networks improves spoken word recognition

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.317127Z

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.

Observation 7713fa01-aa71-4f9d-8140-a0605a1f1736 · outbound

This paper cites Adaptive axonal delays in feedforward spiking neural networks for accurate spoken word recognition.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Adaptive axonal delays in feedforward spiking neural networks for accurate spoken word recognition

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.298869Z

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.

Observation 1e126479-25a3-46ce-81fd-80bede44f0bb · outbound

This paper cites an unresolved cited work.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:37:56.277128Z

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.

Observation dc3c3c4b-e46f-49bc-a24a-5bf0ce863237 · outbound

This paper cites A new spiking convolutional recurrent neural network (scrnn) with applications to event-based hand gesture recognition.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks A new spiking convolutional recurrent neural network (scrnn) with applications to event-based hand gesture recognition

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.258594Z

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.

Observation bc9f2307-6cc4-46e8-b682-a43b005e5cf1 · outbound

This paper cites Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T14:37:56.101352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 997e369b-8367-45f8-aafb-21fd82b37d7d · outbound

This paper cites Long short-term memory with two-compartment spiking neuron, 2023.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Long short-term memory with two-compartment spiking neuron, 2023

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.230566Z

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.

Observation 2f4a0927-b806-4756-8ebf-8281ccbff51d · outbound

This paper cites Spikingformer: Spike-driven residual learning for transformer-based spiking neural network, 2023.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Spikingformer: Spike-driven residual learning for transformer-based spiking neural network, 2023

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.214166Z

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.

Observation a963c4ec-5cc4-4a34-be45-f5707b6fe44c · outbound

This paper cites Ev-flownet: Self-supervised optical flow estimation for event-based cameras.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Ev-flownet: Self-supervised optical flow estimation for event-based cameras

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.198222Z

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.

Observation 1f700e6a-a450-43c5-9850-93c8f3b11fb8 · outbound

This paper cites Unsupervised event-based learning of optical flow, depth, and egomotion, 2018 b.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks Unsupervised event-based learning of optical flow, depth, and egomotion, 2018 b

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:37:56.181352Z

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.

Pith citing papers

Observation 563a97f4-36e2-4ab7-8ebb-a68cee04a69f · inbound

Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA cites this paper.

Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-23T00:35:15.144482Z

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.

Observation d0deea5c-c91f-438c-b385-b9f4c7db0b87 · inbound

End-to-End Keyword Spotting on FPGA Using Graph Neural Networks with a Neuromorphic Auditory Sensor cites this paper.

End-to-End Keyword Spotting on FPGA Using Graph Neural Networks with a Neuromorphic Auditory Sensor TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.499514Z

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.