Data to 14 Oct 2026

Going Deeper in Spiking Neural Networks: VGG and Residual Architectures

arXiv 1802.02627 ↗First posted 7 Feb 2018cs.CVcited by 245

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Abstract (arXiv)

Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware. However, their application in machine learning have largely been limited to very shallow neural network architectures for simple problems. In this paper, we propose a novel algorithmic technique for generating an SNN with a deep architecture, and demonstrate its effectiveness on complex visual recognition problems such as CIFAR-10 and ImageNet. Our technique applies to both VGG and Residual network architectures, with significantly better accuracy than the state-of-the-art. Finally, we present analysis of the sparse event-driven computations to demonstrate reduced hardware overhead when operating in the spiking domain.

Key facts. Cited by 245 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Feb 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 40; 2022: 52; 2023: 57; 2024: 34; 2025: 39; 2026: 23.

245 papers.

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2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 198
2023SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligencecited by 169
2021Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networkscited by 139
2021A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibrationcited by 117
2021Accurate and efficient time-domain classification with adaptive spiking recurrent neural networkscited by 115
2022Attention Spiking Neural Networkscited by 107
2022Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representationcited by 89
2022InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Imagescited by 82
2021Advancing Spiking Neural Networks towards Deep Residual Learningcited by 79
2021Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networkscited by 78
2023Deep Directly-Trained Spiking Neural Networks for Object Detectioncited by 77
2021HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noisecited by 65

38 more

2022GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networkscited by 63
2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
2021Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentationcited by 55
2022Neural Architecture Search for Spiking Neural Networkscited by 53
2022Online Training Through Time for Spiking Neural Networkscited by 49
2023Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANNcited by 46
2023Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillationcited by 44
2022Optimized Potential Initialization for Low-latency Spiking Neural Networkscited by 41
2023SEENN: Towards Temporal Spiking Early-Exit Neural Networkscited by 41
2021Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium Statecited by 40
2022AutoSNN: Towards Energy-Efficient Spiking Neural Networkscited by 40
2023Reducing ANN-SNN Conversion Error through Residual Membrane Potentialcited by 40
2023Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikescited by 40
2023Ternary Spike: Learning Ternary Spikes for Spiking Neural Networkscited by 38
2023Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networkscited by 37
2023Membrane Potential Batch Normalization for Spiking Neural Networkscited by 35
2021A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detectioncited by 31
2022Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikescited by 30
2023RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networkscited by 28
2021Visual Explanations from Spiking Neural Networks using Interspike Intervalscited by 27
2021Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learningcited by 27
2023Training Full Spike Neural Networks via Auxiliary Accumulation Pathwaycited by 26
2021Human-Level Control through Directly-Trained Deep Spiking Q-Networkscited by 25
2022Exploring Temporal Information Dynamics in Spiking Neural Networkscited by 24
2021Federated Learning with Spiking Neural Networkscited by 23
2021Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networkscited by 23
2021Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Visioncited by 22
2022Real Spike: Learning Real-valued Spikes for Spiking Neural Networkscited by 22
2021Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networkscited by 21
2022Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networkscited by 21
2022STSC-SNN: Spatio-Temporal Synaptic Connection with Temporal Convolution and Attention for Spiking Neural Networkscited by 20
2023HTNet for micro-expression recognitioncited by 20
2021The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardwarecited by 19
2022Spikeformer: A Novel Architecture for Training High-Performance Low-Latency Spiking Neural Networkcited by 19
2022Spiking Neural Networks for Frame-based and Event-based Single Object Localizationcited by 18
2021One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latencycited by 17
2022Brain-inspired Multilayer Perceptron with Spiking Neuronscited by 16
2021PrivateSNN: Privacy-Preserving Spiking Neural Networkscited by 15

50 most cited of 245 All 245 (JSON)