mlsanityData to 7 Oct 2026

RIPE-MambaSpike: Resolution-Independent Spiking-State-Space Interfaces for Parameter-Efficient Event-Based Vision

arXiv 2609.32537 ↗First posted 26 Sep 2026cs.CV cs.NEcited by 0

On the map: State-space sequence models (S4, Mamba) → Vision Mamba backbones also fits: Video, motion and generative Mamba models

Abstract (arXiv)

Spiking-Mamba hybrids reach strong accuracy on event-based vision, but existing designs often require tens of millions of parameters. Much of that cost comes from how the spiking front-end is connected to the state-space backbone rather than from the hybrid architecture itself. In a representative model, a single resolution-dependent projection accounts for 33.55M of 36.25M parameters. To that end, we introduce RIPE-MambaSpike (Resolution-Independent, Parameter-Efficient), which replaces that projection with a hierarchical multi-resolution bridge of fixed channel width. Its deployed footprint is 0.870M parameters, constant at fixed time steps and widths across a 43x range of input areas. Reparameterized spiking stages, temporal decoupled modulation, and a dynamic convex-hull-bounded dual-stream membrane-potential attention preserve accuracy under this compact design. Result-wise, RIPE-MambaSpike is pareto-optimal on CIFAR10-DVS, N-Caltech101, and DailyDVS-200. Notably, on the 200-class DailyDVS-200, a scaled 8.04M configuration achieves 45.7% top-1 accuracy, the best reported spiking result on that benchmark, and outperforms prior spiking methods with 3.0-15.1x fewer parameters than dense ANNs. Overall, our findings demonstrate that competitive event-based recognition does not require resolution-dependent parameter growth. Code is available at https://github.com/MuhiminOsim/RIPE-MambaSpike.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Vision Mamba backbones, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 36 of its 45 references that are in the map. First posted on arXiv 26 Sep 2026.

Builds on

36 of 45 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model17 Jan

Other references · 35

  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  2. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  3. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 1,979
  4. 2020Rethinking Attention with Performerscited by 1,584
  5. 2019Event-based Vision: A Surveycited by 1,010
  6. 2024VMamba: Visual State Space Modelcited by 955
  7. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  8. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 520
  9. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 340
  10. 2021Deep Residual Learning in Spiking Neural Networkscited by 326
  11. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 308
  12. 2022Spikformer: When Spiking Neural Network Meets Transformercited by 263
23 more
  1. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 192
  2. 2023Spike-driven Transformercited by 141
  3. 2015A Simple Way to Initialize Recurrent Networks of Rectified Linear Unitscited by 130
  4. 2022MobileOne: An Improved One millisecond Mobile Backbonecited by 109
  5. 2024Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chipscited by 97
  6. 2020Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratchcited by 76
  7. 2021Advancing Spiking Neural Networks towards Deep Residual Learningcited by 76
  8. 2024QKFormer: Hierarchical Spiking Transformer using Q-K Attentioncited by 46
  9. 2024DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognitioncited by 16
  10. 2024Spiking Transformer with Spatial-Temporal Attentioncited by 16
  11. 2022RepGhost: A Hardware-Efficient Ghost Module via Re-parameterizationcited by 13
  12. 2024Spiking Wavelet Transformercited by 11
  13. 2024Event Stream based Human Action Recognition: A High-Definition Benchmark Dataset and Algorithmscited by 11
  14. 2024Mamba-Spike: Enhancing the Mamba Architecture with a Spiking Front-End for Efficient Temporal Data Processingcited by 11
  15. 2024SpikMamba: When SNN meets Mamba in Event-based Human Action Recognitioncited by 10
  16. 2025TS-SNN: Temporal Shift Module for Spiking Neural Networkscited by 5
  17. 2026TP-Spikformer: Token Pruned Spiking Transformercited by 4
  18. 2025SPARTA: Advancing Sparse Attention in Spiking Neural Networks via Spike-Timing-Based Prioritizationcited by 2
  19. 2025SpikePool: Event-driven Spiking Transformer with Pooling Attentioncited by 2
  20. 2024RTFormer: Re-parameter TSBN Spiking Transformercited by 1
  21. 2025FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learningcited by 1
  22. 2026Vision SmolMamba: Spike-Guided Token Pruning for Energy-Efficient Spiking State-Space Vision Modelscited by 1
  23. 2026DarkShake-DVS: Event-based Human Action Recognition under Low-light andShaking Camera Conditionscited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026SpikeWorld: Fast-State Adaptation for Frozen Spiking World Models
  2. 2025Memba: Membrane-driven Parameter-Efficient Fine-Tuning for Mamba
  3. 2026REACT: A Fully Spiking State-Space Model for Real-Time Event-Driven Temporal Perception
  4. 2025Efficient Spiking Point Mamba for Point Cloud Analysis
  5. 2025Vision-Proprioception Fusion with Mamba2 in End-to-End Reinforcement Learning for Motion Control
  6. 2026SpikingVLA: Asynchronous Spiking Vision-Language-Action Models
  7. 2026SpikingBrain2.0: Brain-Inspired Foundation Models for Efficient Long-Context and Cross-Platform Inference
  8. 2024Demystify Mamba in Vision: A Linear Attention Perspective