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NSPDI-SNN: An efficient lightweight SNN based on nonlinear synaptic pruning and dendritic integration

arXiv 2508.21566 ↗First posted 29 Aug 2025q-bio.NC cs.AI cs.NEcited by 0

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Abstract (arXiv)

Spiking neural networks (SNNs) are artificial neural networks based on simulated biological neurons and have attracted much attention in recent artificial intelligence technology studies. The dendrites in biological neurons have efficient information processing ability and computational power; however, the neurons of SNNs rarely match the complex structure of the dendrites. Inspired by the nonlinear structure and highly sparse properties of neuronal dendrites, in this study, we propose an efficient, lightweight SNN method with nonlinear pruning and dendritic integration (NSPDI-SNN). In this method, we introduce nonlinear dendritic integration (NDI) to improve the representation of the spatiotemporal information of neurons. We implement heterogeneous state transition ratios of dendritic spines and construct a new and flexible nonlinear synaptic pruning (NSP) method to achieve the high sparsity of SNN. We conducted systematic experiments on three benchmark datasets (DVS128 Gesture, CIFAR10-DVS, and CIFAR10) and extended the evaluation to two complex tasks (speech recognition and reinforcement learning-based maze navigation task). Across all tasks, NSPDI-SNN consistently achieved high sparsity with minimal performance degradation. In particular, our method achieved the best experimental results on all three event stream datasets. Further analysis showed that NSPDI significantly improved the efficiency of synaptic information transfer as sparsity increased. In conclusion, our results indicate that the complex structure and nonlinear computation of neuronal dendrites provide a promising approach for developing efficient SNN methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 59 references that are in the map. First posted on arXiv 29 Aug 2025.

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References · 20

  1. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 406
  2. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 345
  3. 2018Deep Learning in Spiking Neural Networkscited by 263
  4. 2018Direct Training for Spiking Neural Networks: Faster, Larger, Bettercited by 233
  5. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 177
  6. 2019The Heidelberg spiking datasets for the systematic evaluation of spiking neural networkscited by 137
  7. 2017STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient Recognitioncited by 46
  8. 2019Comprehensive SNN Compression Using ADMM Optimization and Activity Regularizationcited by 45
  9. 2021Pruning of Deep Spiking Neural Networks through Gradient Rewiringcited by 43
  10. 2022Masked Spiking Transformercited by 39
  11. 2023ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networkscited by 23
  12. 2022QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Explorationcited by 21
8 more
  1. 2022Biologically Inspired Dynamic Thresholds for Spiking Neural Networkscited by 19
  2. 2020Dendrite Net: A White-Box Module for Classification, Regression, and System Identificationcited by 11
  3. 2024Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruningcited by 10
  4. 2022A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networkscited by 9
  5. 2020Connection Pruning for Deep Spiking Neural Networks with On-Chip Learningcited by 8
  6. 2022Cyclical Pruning for Sparse Neural Networkscited by 6
  7. 2023Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networkscited by 3
  8. 2018Layer-wise synapse optimization for implementing neural networks on general neuromorphic architecturescited by 1

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  1. 2024Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networks
  2. 2024SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models
  3. 2023Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks
  4. 2023Fully Spiking Denoising Diffusion Implicit Models
  5. 2024SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning
  6. 2024LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
  7. 2025SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning
  8. 2025Spiking Brain Compression: Post-Training Second-order Compression for Spiking Neural Networks