mlsanityData to 7 Oct 2026

Linear Leaky-Integrate-and-Fire Neuron Model Based Spiking Neural Networks and Its Mapping Relationship to Deep Neural Networks

arXiv 2207.04889 ↗First posted 31 May 2022cs.NE cs.AI cs.LGcited by 8

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Abstract (arXiv)

Spiking neural networks (SNNs) are brain-inspired machine learning algorithms with merits such as biological plausibility and unsupervised learning capability. Previous works have shown that converting Artificial Neural Networks (ANNs) into SNNs is a practical and efficient approach for implementing an SNN. However, the basic principle and theoretical groundwork are lacking for training a non-accuracy-loss SNN. This paper establishes a precise mathematical mapping between the biological parameters of the Linear Leaky-Integrate-and-Fire model (LIF)/SNNs and the parameters of ReLU-AN/Deep Neural Networks (DNNs). Such mapping relationship is analytically proven under certain conditions and demonstrated by simulation and real data experiments. It can serve as the theoretical basis for the potential combination of the respective merits of the two categories of neural networks.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 23 of its 73 references that are in the map. First posted on arXiv 31 May 2022.

Builds on

23 of 73 references.

References · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  8. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 236
  9. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 174
  10. 2020Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagationcited by 142
  11. 2020RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Networkcited by 141
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11 more
  1. 2016Towards an integration of deep learning and neurosciencecited by 77
  2. 2020Dynamic ReLUcited by 55
  3. 2016Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardwarecited by 39
  4. 2019Biologically plausible deep learning -- but how far can we go with shallow networks?cited by 25
  5. 2016STDP-based spiking deep convolutional neural networks for object recognitioncited by 24
  6. 2017First-spike based visual categorization using reward-modulated STDPcited by 19
  7. 2016Training Spiking Deep Networks for Neuromorphic Hardwarecited by 12
  8. 2019Unsupervised Visual Feature Learning with Spike-timing-dependent Plasticity: How Far are we from Traditional Feature Learning Approaches?cited by 12
  9. 2016Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learningcited by 9
  10. 2017Representation Learning using Event-based STDPcited by 2
  11. 2016Cascaded Subpatch Networks for Effective CNNscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 3; 2025: 2; 2026: 2.

8 papers.

  1. 2022How important are activation functions in regression and classification? A survey, performance comparison, and future directionscited by 29
  2. 2024Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Networkcited by 5
  3. 2024Detection of Fast-Moving Objects with Neuromorphic Hardwarecited by 4
  4. 2025Scaling Up Resonate-and-Fire Networks for Fast Deep Learningcited by 4
  5. 2024Neuromorphic Correlates of Artificial Consciousnesscited by 0
  6. 2025SteganoSNN: SNN-Based Audio-in-Image Steganography with Encryptioncited by 0
  7. 2026Benchmarking Storage Systems for Machine Learning Workloads Using NIO Benchcited by 0
  8. 2026DVA-Neurons: Design and Verification of Adaptive LIF Neurons: From Single-Neuron Dynamics to Multi-Neuron Spiking Networkscited by 0