Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks | Paper Citation Record

Observation 469e7649-def1-431c-81f9-7d8b2f787388 · outbound

This paper cites Spike-train level backpropagation for training deep recurrent spiking neural networks.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Spike-train level backpropagation for training deep recurrent spiking neural networks

Reference 1

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This paper cites Spiking deep residual networks.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Spiking deep residual networks

Reference 2

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This paper cites Dynamic spiking graph neural net- works.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Dynamic spiking graph neural net- works

Reference 3

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This paper cites SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks

Reference 4

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This paper cites Spikformer: When spiking neural network meets transformer.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Spikformer: When spiking neural network meets transformer

Reference 5

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This paper cites Spike-driven transformer.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Spike-driven transformer

Reference 6

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Observation 757c5cb4-000d-4bb5-b4d7-4b8bc25486b8 · outbound

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

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Spikingformer: Spike-driven residual learning for transformer-based spiking neural network

Reference 7

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This paper cites Attention is all you need.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Attention is all you need

Reference 8

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Observation 63af71b0-02a2-4dc0-97f2-223d06f4eb05 · outbound

This paper cites QKFormer: Hierarchical Spiking Transformer using Q-K Attention.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks QKFormer: Hierarchical Spiking Transformer using Q-K Attention

Reference 9

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This paper cites Masked spiking transformer.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Masked spiking transformer

Reference 10

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Observation b04f0639-f027-421c-8ec0-37517cc2cfff · outbound

This paper cites Attention-free Spikformer: Mixing Spike Sequences with Simple Linear Transforms.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Attention-free Spikformer: Mixing Spike Sequences with Simple Linear Transforms

Reference 11

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 12

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This paper cites Cf-vit: A general coarse-to- fine method for vision transformer.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Cf-vit: A general coarse-to- fine method for vision transformer

Reference 13

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Observation dacb032b-a631-4a71-ae56-0dc2e62ef7eb · outbound

This paper cites Scaling vision transformers to 22 billion parameters.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Scaling vision transformers to 22 billion parameters

Reference 14

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This paper cites FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 15

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This paper cites Top-down visual attention from analysis by synthesis.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Top-down visual attention from analysis by synthesis

Reference 16

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Observation 7020abd7-987a-486b-959e-a71b9c8534c2 · outbound

This paper cites Riformer: Keep your vision backbone effective but removing token mixer.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Riformer: Keep your vision backbone effective but removing token mixer

Reference 17

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Observation b1ece7f6-a2d8-4e95-a63e-5d1068d13478 · outbound

This paper cites A closer look at self-supervised lightweight vision transformers.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks A closer look at self-supervised lightweight vision transformers

Reference 18

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Observation 999b83e3-0002-47e4-9b1e-ce170ff45acf · outbound

This paper cites E fficientvit: Memory e fficient vision transformer with cascaded group attention.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks E fficientvit: Memory e fficient vision transformer with cascaded group attention

Reference 19

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This paper cites Spikeformer: A Novel Architecture for Training High-Performance Low-Latency Spiking Neural Network.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Spikeformer: A Novel Architecture for Training High-Performance Low-Latency Spiking Neural Network

Reference 20

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This paper cites Deep residual learning in spik- ing neural networks.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Deep residual learning in spik- ing neural networks

Reference 21

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This paper cites Enhancing the Performance of Transformer-based Spiking Neural Networks by SNN-optimized Downsampling with Precise Gradient Backpropagation.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Enhancing the Performance of Transformer-based Spiking Neural Networks by SNN-optimized Downsampling with Precise Gradient Backpropagation

Reference 22

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This paper cites Temporal-wise attention spiking neural networks for event streams classification.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Temporal-wise attention spiking neural networks for event streams classification

Reference 23

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This paper cites Tcja-snn: Temporal-channel joint attention for spiking neural networks.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Tcja-snn: Temporal-channel joint attention for spiking neural networks

Reference 24

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Observation f7c36e0b-9aa4-4f6e-b7d2-9860f501693c · outbound

This paper cites Spatial-temporal self-attention for asyn- chronous spiking neural networks.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Spatial-temporal self-attention for asyn- chronous spiking neural networks

Reference 25

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This paper cites Attention spiking neural networks.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Attention spiking neural networks

Reference 26

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Observation 4f2e99cb-3f7e-41ea-82b5-0755cb06e74c · outbound

This paper cites Stsc-snn: Spatio-temporal synaptic connection with temporal convolution and attention for spiking neural net- works.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Stsc-snn: Spatio-temporal synaptic connection with temporal convolution and attention for spiking neural net- works

Reference 27

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This paper cites DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attention.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attention

Reference 28

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This paper cites A spatial–channel– temporal-fused attention for spiking neural networks.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks A spatial–channel– temporal-fused attention for spiking neural networks

Reference 29

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This paper cites Simple model of spiking neurons.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Simple model of spiking neurons

Reference 30

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This paper cites A quantitative de- scription of membrane current and its application to conduction and excitation in nerve.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks A quantitative de- scription of membrane current and its application to conduction and excitation in nerve

Reference 31

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This paper cites A novel image denoising algorithm combining attention mechanism and residual unet network.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks A novel image denoising algorithm combining attention mechanism and residual unet network

Reference 32

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This paper cites Agent Attention: On the Integration of Softmax and Linear Attention.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Agent Attention: On the Integration of Softmax and Linear Attention

Reference 33

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This paper cites Flatten transformer: Vision transformer using focused linear attention.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Flatten transformer: Vision transformer using focused linear attention

Reference 34

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This paper cites Learning multiple lay- ers of features from tiny images.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Learning multiple lay- ers of features from tiny images

Reference 35

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This paper cites Cifar10-dvs: an event-stream dataset for object classifica- tion.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Cifar10-dvs: an event-stream dataset for object classifica- tion

Reference 36

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

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks A low power, fully event-based gesture recognition system

Reference 37

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This paper cites Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation

Reference 38

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This paper cites Optimizing deeper spiking neural networks for dynamic vision sensing.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Optimizing deeper spiking neural networks for dynamic vision sensing

Reference 39

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Observation 9736b678-ba04-4c46-9575-b63bc3d0257a · outbound

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

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Incorporating learnable membrane time constant to enhance learning of spiking neural networks

Reference 40

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Observation af87e84d-b2bc-4fd0-8b82-d7b2ba707073 · outbound

This paper cites Spatio- temporal backpropagation for training high-performance spik- ing neural networks.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Spatio- temporal backpropagation for training high-performance spik- ing neural networks

Reference 41

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Observation 39455273-3a4d-4b0c-83ac-ad2c5b86680e · outbound

This paper cites Direct training for spiking neural networks: Faster, larger, better.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Direct training for spiking neural networks: Faster, larger, better

Reference 42

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Observation c7927bd0-29b6-403b-aa05-c5f7a577c982 · outbound

This paper cites Temporal spike sequence learn- ing via backpropagation for deep spiking neural networks.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Temporal spike sequence learn- ing via backpropagation for deep spiking neural networks

Reference 43

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Observation 83ca00aa-fa95-40b3-a58f-8a88a5d97907 · outbound

This paper cites Di fferentiable spike: Rethinking gradient-descent for training spiking neural networks.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Di fferentiable spike: Rethinking gradient-descent for training spiking neural networks

Reference 44

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Observation 7050ff9e-1b66-4094-bcb4-7bba2ed7549c · outbound

This paper cites Go- ing deeper with directly-trained larger spiking neural networks.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Go- ing deeper with directly-trained larger spiking neural networks

Reference 45

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Observation 49dde595-c022-4ee5-8c48-8e54ec359d34 · outbound

This paper cites Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting

Reference 46

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Observation eadf0a58-af3f-4c9b-88ff-454b79c44461 · outbound

This paper cites Diet-snn: A low-latency spik- ing neural network with direct input encoding and leakage and threshold optimization.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Diet-snn: A low-latency spik- ing neural network with direct input encoding and leakage and threshold optimization

Reference 47

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Observation 9a1885e6-7c1c-4984-a044-cdb47a4d4c12 · outbound

This paper cites Ad- vancing spiking neural networks toward deep residual learning.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Ad- vancing spiking neural networks toward deep residual learning

Reference 48

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Observation 39b78356-2876-4697-9d21-e6cc6ce20c74 · outbound

This paper cites Training high-performance low-latency spiking neural networks by differentiation on spike representation.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Training high-performance low-latency spiking neural networks by differentiation on spike representation

Reference 49

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Observation c565dd9d-de63-4ad1-bae5-c043a6673376 · outbound

This paper cites Decoupled Weight Decay Regularization.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Decoupled Weight Decay Regularization

Reference 50

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This paper cites Swin transformer: Hier- archical vision transformer using shifted windows.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Swin transformer: Hier- archical vision transformer using shifted windows

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:04:28.311112Z

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Observation d260fecd-c5e4-4965-b868-924a5e8751f8 · outbound

This paper cites Neuronal dynamics: From single neurons to networks and models of cognition.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Neuronal dynamics: From single neurons to networks and models of cognition

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T13:04:27.822051Z

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Observation 07233d81-283f-4756-ba3f-414120e3df65 · outbound

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

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks 1.1 computing’s energy problem (and what we can do about it)

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:04:28.282069Z

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Observation 0b1dbc93-5ae1-4b36-aaa0-4bd848aa32cb · outbound

This paper cites Liaf-net: Leaky integrate and analog fire net- work for lightweight and e fficient spatiotemporal information processing.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Liaf-net: Leaky integrate and analog fire net- work for lightweight and e fficient spatiotemporal information processing

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:04:28.264538Z

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Observation 554ff5bf-299e-4c56-b1bf-1a22c0e6002a · outbound

This paper cites E fficient processing of spatio-temporal data streams with spiking neural networks.

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks E fficient processing of spatio-temporal data streams with spiking neural networks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:04:28.245552Z

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Observation 7ea51e29-e8e9-4465-bb2b-f4b1cd6d77ab · outbound

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

Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks Synaptic plasticity dynamics for deep continuous local learning (decolle)

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:04:28.228767Z

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