Paper Citation Record · LEDGER

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

As of 16 August 2026, this Paper Citation Record lists 84 of 84 outbound references and 3 inbound Pith citation observations for arXiv:2502.09449.

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

pith.paper-citation-record.v1
2502.09449 v1

Coverage vector

measured 84 of 84 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T21:29:16.306080Z

measured 87 of 87 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:41:12.581021Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T16:31:12.265355Z

Reference resolution

84 of 84 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2709780f-6f46-4ff6-9070-855b20926b6b · outbound

This paper cites Bidirectional recurrent neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Bidirectional recurrent neural networks,

Reference 1

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Observation 5a39f32f-62b8-4d8c-894c-623f14e0aa9c · outbound

This paper cites Long short-term memory,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Long short-term memory,

Reference 2

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Observation f4bbc7f6-a1a0-4c81-8081-09a0cd7fe695 · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 3

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Observation 0d70c5b5-9b2e-4e51-accc-6e8fea86ad5f · outbound

This paper cites Attention is all you need,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Attention is all you need,

Reference 4

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Observation 6712094e-a2b7-4bbe-9232-63424a9aae62 · outbound

This paper cites Efficiently modeling long sequences with structured state spaces,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Efficiently modeling long sequences with structured state spaces,

Reference 5

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Observation 5872d78e-ea21-4f46-8bfc-fa490d625834 · outbound

This paper cites Model compression and hardware acceleration for neural networks: A comprehensive survey,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Model compression and hardware acceleration for neural networks: A comprehensive survey,

Reference 6

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Observation b1418ca7-33c0-425c-8734-29024ed61dff · outbound

This paper cites Networks of spiking neurons: The third generation of neural network models,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Networks of spiking neurons: The third generation of neural network models,

Reference 7

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Observation 21bd40b8-f68f-4381-a147-d18625f427f2 · outbound

This paper cites Brain- inspired computing: A systematic survey and future trends,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Brain- inspired computing: A systematic survey and future trends,

Reference 8

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Observation c395f302-294a-4bfd-82ce-c0b944f3a1c6 · outbound

This paper cites Towards spike-based machine intelligence with neuromorphic computing,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Towards spike-based machine intelligence with neuromorphic computing,

Reference 9

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Observation 430ed24e-8cdf-488d-9bbc-f67895412d1b · outbound

This paper cites Rapid feedforward computation by temporal encoding and learning with spiking neurons,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Rapid feedforward computation by temporal encoding and learning with spiking neurons,

Reference 10

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Observation 5cec736f-a675-4ba4-b8af-8f28d85aa85a · outbound

This paper cites Loihi: A neuromorphic manycore processor with on-chip learning,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Loihi: A neuromorphic manycore processor with on-chip learning,

Reference 11

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Observation 0a03c77c-7b07-4a02-bba8-b87c432fa6e9 · outbound

This paper cites Towards artificial general intelligence with hybrid Tianjic chip architecture,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Towards artificial general intelligence with hybrid Tianjic chip architecture,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:17.137862Z

Source-reported events for the cited work

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Observation 3de76dbf-7a0b-4ae5-870b-4d00e5ff6591 · outbound

This paper cites Darwin: A neuromorphic hardware co-processor based on spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Darwin: A neuromorphic hardware co-processor based on spiking neural networks,

Reference 13

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Observation e71a8820-d007-4186-95af-86f90a8524ae · outbound

This paper cites Darwin3: A large-scale neuromorphic chip with a novel ISA and on-chip learning,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Darwin3: A large-scale neuromorphic chip with a novel ISA and on-chip learning,

Reference 14

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Source-reported events for the cited work

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Observation 375452fd-131e-4a9b-ad5c-1de78ea33fbb · outbound

This paper cites Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip,

Reference 15

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Observation e981a11a-16f7-47c9-b576-dcc36a91da14 · outbound

This paper cites An FPGA implementation of deep spiking neural networks for low-power and fast classification,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects An FPGA implementation of deep spiking neural networks for low-power and fast classification,

Reference 16

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Observation f3e33160-e3ad-4844-8f87-c64888fcf24a · outbound

This paper cites Modeling single- neuron dynamics and computations: A balance of detail and abstraction,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Modeling single- neuron dynamics and computations: A balance of detail and abstraction,

Reference 17

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Observation accc33db-7a10-42b2-b338-06d0efa3ba65 · outbound

This paper cites Deep spiking neural networks for large vocabulary automatic speech recognition,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Deep spiking neural networks for large vocabulary automatic speech recognition,

Reference 18

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Observation c2214cfb-ea59-4ee4-86d8-e83503df0cc9 · outbound

This paper cites A spiking neural network framework for robust sound classification,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects A spiking neural network framework for robust sound classification,

Reference 19

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Observation dc18c4ad-adc3-4b62-822c-9b52c506e11f · outbound

This paper cites A spiking neural network system for robust sequence recognition,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects A spiking neural network system for robust sequence recognition,

Reference 20

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Observation 59fa72cb-fcf6-4933-a075-1a904f83a466 · outbound

This paper cites Progressive tandem learning for pattern recognition with deep spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Progressive tandem learning for pattern recognition with deep spiking neural networks,

Reference 21

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Observation 632ff16a-b9cc-480f-99f3-2964061e82f6 · outbound

This paper cites Training spiking neural networks with local tandem learning,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Training spiking neural networks with local tandem learning,

Reference 22

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Observation 3608e992-5c11-4bda-a5c4-f15011ed8c9e · outbound

This paper cites Fast-SNN: Fast spiking neural network by converting quantized ANN,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Fast-SNN: Fast spiking neural network by converting quantized ANN,

Reference 23

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Observation 0e696af5-d4b8-46dc-af5c-44463a909af0 · outbound

This paper cites Constructing deep spiking neural networks from artificial neural networks with knowledge distillation,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Constructing deep spiking neural networks from artificial neural networks with knowledge distillation,

Reference 24

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Observation 65eec745-d8b0-443a-88b7-f2061e059e51 · outbound

This paper cites Temporal efficient training of spiking neural network via gradient re-weighting,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Temporal efficient training of spiking neural network via gradient re-weighting,

Reference 25

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Observation df0d7e17-d028-4a2e-ba91-2e247f698ab2 · outbound

This paper cites IM-Loss: Information maximization loss for spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects IM-Loss: Information maximization loss for spiking neural networks,

Reference 26

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Observation 843159f3-b6b8-493e-b76e-e9e4b32eb73b · outbound

This paper cites Learnable surrogate gradient for direct training spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Learnable surrogate gradient for direct training spiking neural networks,

Reference 27

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Observation 156ebac6-c1de-49d8-86d5-a8d53886ec91 · outbound

This paper cites Online stabilization of spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Online stabilization of spiking neural networks,

Reference 28

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Observation 6f2117d1-e7bb-4d81-836d-097229814129 · outbound

This paper cites NDOT: Neuronal dynamics-based online training for spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects NDOT: Neuronal dynamics-based online training for spiking neural networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.939733Z

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Observation 4c64e133-47cc-42d4-8a19-0c613d181970 · outbound

This paper cites Rethinking the membrane dynamics and optimization objectives of spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Rethinking the membrane dynamics and optimization objectives of spiking neural networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.928166Z

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Observation 17fe985f-74d4-451f-9984-5fa7620ea693 · outbound

This paper cites A hybrid neural coding approach for pattern recognition with spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects A hybrid neural coding approach for pattern recognition with spiking neural networks,

Reference 31

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Observation 6347d2d4-4fee-4078-9b36-78d4da7ceb11 · outbound

This paper cites Attention spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Attention spiking neural networks,

Reference 32

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Observation 11fc5932-6127-446e-afcd-d982b61cf191 · outbound

This paper cites Enhancing adaptive history reserving by spiking convolutional block attention module in recurrent neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Enhancing adaptive history reserving by spiking convolutional block attention module in recurrent neural networks,

Reference 33

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Observation 9f4f90a6-5e29-4a9d-8567-8151eda82041 · outbound

This paper cites Synaptic learning with augmented spikes,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Synaptic learning with augmented spikes,

Reference 34

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Observation 833f707e-b94a-4fc9-b9dd-48e830001e6b · outbound

This paper cites Ternary spike: Learning ternary spikes for spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Ternary spike: Learning ternary spikes for spiking neural networks,

Reference 35

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Observation eae5d363-6d83-4b91-989c-5198ecd22c4d · outbound

This paper cites SpikeLM: Towards general spike-driven language modeling via elastic bi-spiking mechanisms,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects SpikeLM: Towards general spike-driven language modeling via elastic bi-spiking mechanisms,

Reference 36

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Observation 4b4522b0-a4ae-4cd0-a50e-bde1aad2c0e3 · outbound

This paper cites Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions

Reference 37

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Observation 1d8e1c9d-8d84-467e-8f25-267928bdaba8 · outbound

This paper cites Gradient-based learning applied to document recognition,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Gradient-based learning applied to document recognition,

Reference 38

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Observation d8517362-87bf-46f7-a3a4-8d785732ff75 · outbound

This paper cites Learning multiple layers of features from tiny images,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Learning multiple layers of features from tiny images,

Reference 39

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Observation a99359f2-6525-4a03-be27-948680e968ac · outbound

This paper cites Training spiking neural networks using lessons from deep learning,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Training spiking neural networks using lessons from deep learning,

Reference 40

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Observation ed7e22a4-6bcc-4144-b5a4-18cb85dc9013 · outbound

This paper cites Converting static image datasets to spiking neuromorphic datasets using saccades,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Converting static image datasets to spiking neuromorphic datasets using saccades,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.824279Z

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Observation 0b3b1af8-abab-4ef4-b87b-826c1f42cb6b · outbound

This paper cites CIFAR10-DVS: An event-stream dataset for object classification,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects CIFAR10-DVS: An event-stream dataset for object classification,

Reference 42

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Observation 2dace7c4-fd55-4dc5-832d-36b9db8163b8 · outbound

This paper cites A low power, fully event-based gesture recognition system,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects A low power, fully event-based gesture recognition system,

Reference 43

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Observation 159ae192-4395-4298-bb1e-ec07c6731baa · outbound

This paper cites Enhancing SNN-based spatio-temporal learning: A benchmark dataset and cross-modality attention model,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Enhancing SNN-based spatio-temporal learning: A benchmark dataset and cross-modality attention model,

Reference 44

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Observation 51c398d8-e584-4076-a006-fe1f2f9b1602 · outbound

This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 45

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Observation 53596eec-7b2c-4fb1-8f3c-bbcb39cdaef0 · outbound

This paper cites DARPA TIMIT acoustic-phonetic continuous speech corpus CD-ROM. NIST speech disc 1-1.1,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects DARPA TIMIT acoustic-phonetic continuous speech corpus CD-ROM. NIST speech disc 1-1.1,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.781220Z

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Observation 9a60887c-5d14-4b1d-8e7c-bba80467b7de · outbound

This paper cites An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.769173Z

Source-reported events for the cited work

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Observation 2b22b147-bd87-4f83-9958-057016bba2bd · outbound

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

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects The heidelberg spiking data sets for the systematic evaluation of spiking neural networks,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.757693Z

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Observation 5de7b799-9350-4692-9bb9-8d162502026f · outbound

This paper cites Spatio-temporal backpropa- gation for training high-performance spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Spatio-temporal backpropa- gation for training high-performance spiking neural networks,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.746528Z

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Observation 926f8049-152f-4611-93a3-038a8b8523da · outbound

This paper cites Online training through time for spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Online training through time for spiking neural networks,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.734866Z

Source-reported events for the cited work

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Observation d8f060eb-87cc-4c6a-992f-fd52556853ee · outbound

This paper cites Towards memory-and time-efficient backpropagation for training spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Towards memory-and time-efficient backpropagation for training spiking neural networks,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.724013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 84487c5c-9889-4af9-a510-09c9211d273c · outbound

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

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects A surrogate gradient spiking baseline for speech command recognition,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.705695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ef8bcd30-4ec4-4891-a99a-f346d0e5c9e6 · outbound

This paper cites TC-LIF: A two- compartment spiking neuron model for long-term sequential modelling,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects TC-LIF: A two- compartment spiking neuron model for long-term sequential modelling,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.695226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 58766ed3-5f34-4531-8bc5-71b36243582a · outbound

This paper cites A progressive training framework for spiking neural networks with learnable multi- hierarchical model,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects A progressive training framework for spiking neural networks with learnable multi- hierarchical model,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.684613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b51fc254-46cc-47fa-aba9-e6bf08fb8c10 · outbound

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

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Incorporating learnable membrane time constant to enhance learning of spiking neural networks,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.674231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e7dca8e9-9e82-416c-8c5f-909d0ba159ec · outbound

This paper cites GLIF: A unified gated leaky integrate-and-fire neuron for spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects GLIF: A unified gated leaky integrate-and-fire neuron for spiking neural networks,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.662583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b2bd7db5-eb5c-4d09-8c11-49ac006a21a1 · outbound

This paper cites PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T21:29:16.199311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f9dd008-77e8-441a-9ac2-6ba15ffc1c33 · outbound

This paper cites Accurate online training of dynamical spiking neural networks through forward propagation through time,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Accurate online training of dynamical spiking neural networks through forward propagation through time,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.652344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ca70f846-c8e6-45b7-923c-0a75cb9a37e0 · outbound

This paper cites Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.642070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7f894805-ccb7-4b2e-a6f1-82e6ecbb0243 · outbound

This paper cites A review of the integrate-and-fire neuron model: I. homogeneous synaptic input,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects A review of the integrate-and-fire neuron model: I. homogeneous synaptic input,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.631778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 62b50e4c-9e7b-48b2-9032-5e5b494dc3c6 · outbound

This paper cites Temporal effective batch normalization in spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Temporal effective batch normalization in spiking neural networks,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.622003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 74b1652d-2e00-4166-a9ae-f6cb5910de69 · outbound

This paper cites Adaptive smoothing gra- dient learning for spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Adaptive smoothing gra- dient learning for spiking neural networks,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.611461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1f96d722-72a0-4d63-abe6-e103892ba97d · outbound

This paper cites Building a large annotated corpus of english: The Penn Treebank,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Building a large annotated corpus of english: The Penn Treebank,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.599988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7e3acab5-eb79-47c4-85c0-46a87185c474 · outbound

This paper cites Long short-term memory and learning-to-learn in networks of spiking neurons,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Long short-term memory and learning-to-learn in networks of spiking neurons,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.589021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a5d38091-2efe-4283-9061-4a5e2d6cf70f · outbound

This paper cites A solution to the learning dilemma for recurrent networks of spiking neurons,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects A solution to the learning dilemma for recurrent networks of spiking neurons,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.578509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5e33ba2d-800d-46cb-8b27-eba2c3af929b · outbound

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

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.567037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9744e774-d8cc-4a41-980a-19e6da8441c1 · outbound

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

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based opti- mization to spiking neural networks,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T21:29:16.239204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48284526-d3d2-4ad2-a645-f4a8940aeb6c · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T21:29:16.243207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dc3193a-0bb1-47d9-bb50-a33d14bde5c0 · outbound

This paper cites Going deeper with directly-trained larger spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Going deeper with directly-trained larger spiking neural networks,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.556767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4cdf9267-9a42-433d-8b19-e8c492269723 · outbound

This paper cites Layer Normalization.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Layer Normalization

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T21:29:16.252316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae60813a-666f-4ac9-bc43-a6f4def6303e · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.546372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 05df04c3-ef8c-42fb-b6e7-6c5434d3e068 · outbound

This paper cites Parallel spiking neurons with high efficiency and ability to learn long-term dependencies,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Parallel spiking neurons with high efficiency and ability to learn long-term dependencies,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.534646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4120bcdb-011e-4bb7-b02b-3ed40a73ebe7 · outbound

This paper cites CLIF: Complementary leaky integrate-and-fire neuron for spiking neural networks,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects CLIF: Complementary leaky integrate-and-fire neuron for spiking neural networks,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.522468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cec21bbd-8b83-4f4d-81d1-a08ac2ccb729 · outbound

This paper cites Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T21:29:16.272100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e99f640-7673-485e-9909-f6733bff32bb · outbound

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

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Learning delays in spiking neural networks using dilated convolutions with learnable spacings,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.501155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dabafe5c-7227-4cbf-beee-14920d6a0f29 · outbound

This paper cites Towards Ultra-Low-Power Neuromorphic Speech Enhancement with Spiking-FullSubNet.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Towards Ultra-Low-Power Neuromorphic Speech Enhancement with Spiking-FullSubNet

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T21:29:16.280079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 119a65f0-be88-40b9-a1b0-de251ebddaeb · outbound

This paper cites Spike- driven transformer,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Spike- driven transformer,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.488994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3bf743e2-84fd-4a76-9546-a23a327df66f · outbound

This paper cites SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.477173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0eb39d62-9dcc-4ccb-8f1d-aca80761f8e3 · outbound

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

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.466807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ce3d53cc-93fc-4b4a-b67a-85be8f7c78e9 · outbound

This paper cites Decoupled Weight Decay Regularization.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Decoupled Weight Decay Regularization

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T21:29:16.294608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c23dd99-0ca8-4e70-bcb0-968b64b839e9 · outbound

This paper cites Regularizing and optimizing LSTM language models,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Regularizing and optimizing LSTM language models,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.455881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 076565d8-983c-446f-bda0-fcd2d06d7f76 · outbound

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

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects 1.1 computing’s energy problem (and what we can do about it),

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.444865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2dca9e04-8cf6-48f1-914a-ab37759c21ee · outbound

This paper cites Towards scalable GPU- accelerated SNN training via temporal fusion,.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects Towards scalable GPU- accelerated SNN training via temporal fusion,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.430934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d12a78a1-c06d-4794-8683-097c0ad6bdab · outbound

This paper cites 19 949–19 972.

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects 19 949–19 972

Reference 235

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:29:16.511782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation 49ead3c1-b979-4c44-a35f-7dbcc07da985 · inbound

The Promise of Spiking Neural Networks for Ubiquitous Computing: A Survey and New Perspectives cites this paper.

The Promise of Spiking Neural Networks for Ubiquitous Computing: A Survey and New Perspectives Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

Reference 132

Resolution: unresolved; no resolver link, observed 2026-08-07T11:41:12.581021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee902cd1-29d5-4571-8cf1-542c2b6a64a6 · inbound

Detecting AI-Generated Videos with Spiking Neural Networks cites this paper.

Detecting AI-Generated Videos with Spiking Neural Networks Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:31:12.272352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c8c7cc60-2f48-4f34-9c06-7d94fcc4a9fb · inbound

Detecting AI-Generated Videos with Spiking Neural Networks cites this paper.

Detecting AI-Generated Videos with Spiking Neural Networks Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T02:24:59.727747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.