mlsanityData to 7 Oct 2026

A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practices

arXiv 2303.10780 ↗First posted 19 Mar 2023cs.NE cs.LG eess.IVcited by 8

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Abstract (arXiv)

Biological neural networks continue to inspire breakthroughs in neural network performance. And yet, one key area of neural computation that has been under-appreciated and under-investigated is biologically plausible, energy-efficient spiking neural networks, whose potential is especially attractive for low-power, mobile, or otherwise hardware-constrained settings. We present a literature review of recent developments in the interpretation, optimization, efficiency, and accuracy of spiking neural networks. Key contributions include identification, discussion, and comparison of cutting-edge methods in spiking neural network optimization, energy-efficiency, and evaluation, starting from first principles so as to be accessible to new practitioners.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 27 of its 67 references that are in the map. First posted on arXiv 19 Mar 2023.

Builds on

27 of 67 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  4. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  5. 2021Convex optimizationcited by 2,479
  6. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  7. 2021Deep Residual Learning in Spiking Neural Networkscited by 326
  8. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 308
  9. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 268
  10. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 251
  11. 2018HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classificationcited by 215
  12. 2017SuperSpike: Supervised learning in multi-layer spiking neural networkscited by 178
15 more
  1. 2019The Heidelberg spiking datasets for the systematic evaluation of spiking neural networkscited by 135
  2. 2020Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratchcited by 76
  3. 2021HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noisecited by 61
  4. 2022Online Training Through Time for Spiking Neural Networkscited by 48
  5. 2018BindsNET: A machine learning-oriented spiking neural networks library in Pythoncited by 42
  6. 2021Sparse Spiking Gradient Descentcited by 41
  7. 2021Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium Statecited by 39
  8. 2020Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameterscited by 27
  9. 2019SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuroncited by 23
  10. 2022Fluctuation-driven initialization for spiking neural network trainingcited by 16
  11. 2018Is Neuromorphic MNIST neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domaincited by 15
  12. 2021Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneckcited by 15
  13. 2018NengoDL: Combining deep learning and neuromorphic modelling methodscited by 11
  14. 2022An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interfacecited by 8
  15. 2017Learning and Real-time Classification of Hand-written Digits With Spiking Neural Networkscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 4; 2026: 1.

8 papers.

  1. 2023Learning Long Sequences in Spiking Neural Networkscited by 19
  2. 2023A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networkscited by 6
  3. 2024Parallel Hyperparameter Optimization Of Spiking Neural Networkcited by 1
  4. 2025DRiVE: Dynamic Recognition in VEhicles using snnTorchcited by 0
  5. 2025TDFormer: A Top-Down Attention-Controlled Spiking Transformercited by 0
  6. 2025A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networkscited by 0
  7. 2025SteganoSNN: SNN-Based Audio-in-Image Steganography with Encryptioncited by 0
  8. 2026Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networkscited by 0