mlsanityData to 7 Oct 2026

DA-LIF: Dual Adaptive Leaky Integrate-and-Fire Model for Deep Spiking Neural Networks

arXiv 2502.10422 ↗First posted 5 Feb 2025cs.NE cs.AIcited by 8

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Abstract (arXiv)

Spiking Neural Networks (SNNs) are valued for their ability to process spatio-temporal information efficiently, offering biological plausibility, low energy consumption, and compatibility with neuromorphic hardware. However, the commonly used Leaky Integrate-and-Fire (LIF) model overlooks neuron heterogeneity and independently processes spatial and temporal information, limiting the expressive power of SNNs. In this paper, we propose the Dual Adaptive Leaky Integrate-and-Fire (DA-LIF) model, which introduces spatial and temporal tuning with independently learnable decays. Evaluations on both static (CIFAR10/100, ImageNet) and neuromorphic datasets (CIFAR10-DVS, DVS128 Gesture) demonstrate superior accuracy with fewer timesteps compared to state-of-the-art methods. Importantly, DA-LIF achieves these improvements with minimal additional parameters, maintaining low energy consumption. Extensive ablation studies further highlight the robustness and effectiveness of the DA-LIF model.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 5 Feb 2025.

Builds on

17 of 36 references.

References · 17

  1. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  2. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 340
  3. 2021Deep Residual Learning in Spiking Neural Networkscited by 326
  4. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 268
  5. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 192
  6. 2018Long short-term memory and learning-to-learn in networks of spiking neuronscited by 179
  7. 2021Temporal-wise Attention Spiking Neural Networks for Event Streams Classificationcited by 106
  8. 2022Attention Spiking Neural Networkscited by 103
  9. 2021Advancing Spiking Neural Networks towards Deep Residual Learningcited by 76
  10. 2022GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networkscited by 63
  11. 2023Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillationcited by 41
  12. 2023Membrane Potential Batch Normalization for Spiking Neural Networkscited by 35
5 more
  1. 2022Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networkscited by 21
  2. 2022Biologically Inspired Dynamic Thresholds for Spiking Neural Networkscited by 18
  3. 2023Inherent Redundancy in Spiking Neural Networkscited by 13
  4. 2023Tensor Decomposition Based Attention Module for Spiking Neural Networkscited by 8
  5. 2023Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technologycited by 3

Built on by

Cited by year (2026 to 7 Oct)

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

8 papers.

  1. 2025Edge Intelligence with Spiking Neural Networkscited by 2
  2. 2025AR-LIF: Adaptive reset leaky integrate-and-fire neuron for spiking neural networkscited by 2
  3. 2025Head-Tail-Aware KL Divergence in Knowledge Distillation for Spiking Neural Networkscited by 1
  4. 2026ChronoSpike: An Adaptive Spiking Graph Neural Network for Dynamic Graphscited by 1
  5. 2025Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fieldscited by 0
  6. 2026On the Sensitivity of Firing Rate-Based Federated Spiking Neural Networks to Differential Privacycited by 0
  7. 2026Learnable Burst Quantization for Expressive and Efficient Spiking Neural Networkscited by 0
  8. 2026QDS-SNN: Energy-efficient Quantum Deeply-Supervised Spiking Neural Network Algorithm for Traffic Sign Recognitioncited by 0