mlsanityData to 14 Oct 2026

TIM: An Efficient Temporal Interaction Module for Spiking Transformer

arXiv 2401.11687 ↗First posted 22 Jan 2024cs.NE cs.CV cs.LGcited by 12

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Abstract (arXiv)

Spiking Neural Networks (SNNs), as the third generation of neural networks, have gained prominence for their biological plausibility and computational efficiency, especially in processing diverse datasets. The integration of attention mechanisms, inspired by advancements in neural network architectures, has led to the development of Spiking Transformers. These have shown promise in enhancing SNNs' capabilities, particularly in the realms of both static and neuromorphic datasets. Despite their progress, a discernible gap exists in these systems, specifically in the Spiking Self Attention (SSA) mechanism's effectiveness in leveraging the temporal processing potential of SNNs. To address this, we introduce the Temporal Interaction Module (TIM), a novel, convolution-based enhancement designed to augment the temporal data processing abilities within SNN architectures. TIM's integration into existing SNN frameworks is seamless and efficient, requiring minimal additional parameters while significantly boosting their temporal information handling capabilities. Through rigorous experimentation, TIM has demonstrated its effectiveness in exploiting temporal information, leading to state-of-the-art performance across various neuromorphic datasets. The code is available at https://github.com/BrainCog-X/Brain-Cog/tree/main/examples/TIM.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 29 of its 48 references that are in the map. First posted on arXiv 22 Jan 2024.

Builds on

29 of 48 references.

References · 29

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2019Event-based Vision: A Surveycited by 1,016
  4. 2017Learning Spatio-Temporal Representation with Pseudo-3D Residual Networkscited by 387
  5. 2019High Speed and High Dynamic Range Video with an Event Cameracited by 345
  6. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 345
  7. 2021Deep Residual Learning in Spiking Neural Networkscited by 331
  8. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 314
  9. 2022Spikformer: When Spiking Neural Network Meets Transformercited by 271
  10. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 245
  11. 2019End-to-End Learning of Representations for Asynchronous Event-Based Datacited by 217
  12. 2021Temporal-wise Attention Spiking Neural Networks for Event Streams Classificationcited by 106
17 more
  1. 2023Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarkscited by 90
  2. 2022Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representationcited by 89
  3. 2021Advancing Spiking Neural Networks towards Deep Residual Learningcited by 79
  4. 2020Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Networkcited by 37
  5. 2022TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networkscited by 35
  6. 2022BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulationcited by 33
  7. 2023SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognitioncited by 30
  8. 2020Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networkscited by 28
  9. 2022Exploring Temporal Information Dynamics in Spiking Neural Networkscited by 24
  10. 2021Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networkscited by 23
  11. 2022Spikeformer: A Novel Architecture for Training High-Performance Low-Latency Spiking Neural Networkcited by 19
  12. 2022An Unsupervised STDP-based Spiking Neural Network Inspired By Biologically Plausible Learning Rules and Connectionscited by 13
  13. 2023Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computingcited by 11
  14. 2023Improving Stability and Performance of Spiking Neural Networks through Enhancing Temporal Consistencycited by 10
  15. 2022EventMix: An Efficient Augmentation Strategy for Event-Based Datacited by 6
  16. 2023DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attentioncited by 4
  17. 2023Attention-free Spikformer: Mixing Spike Sequences with Simple Linear Transformscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 4.

12 papers.

  1. 2024Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMambacited by 19
  2. 2024EZSR: Event-based Zero-Shot Recognitioncited by 8
  3. 2024Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directionscited by 7
  4. 2024Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillationcited by 2
  5. 2025Edge Intelligence with Spiking Neural Networkscited by 2
  6. 2025FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learningcited by 1
  7. 2026Fire on Motion: Optimizing Video Pass-bands for Efficient Spiking Action Recognitioncited by 1
  8. 2026Breaking Global Self-Attention Bottlenecks in Transformer-based Spiking Neural Networks with Local Structure-Aware Self-Attentioncited by 1
  9. 2025Incorporating the Refractory Period into Spiking Neural Networks through Spike-Triggered Threshold Dynamicscited by 0
  10. 2025CogniSNN: Enabling Neuron-Expandability, Pathway-Reusability, and Dynamic-Configurability with Random Graph Architectures in Spiking Neural Networkscited by 0
  11. 2026Stable Spike: Dual Consistency Optimization via Bitwise AND Operations for Spiking Neural Networkscited by 0
  12. 2026Spiking Local Interaction and Adaptive Complementary Fusion for Spiking Transformercited by 0