ReviewFrontiers in neuroscience2024
Direct training high-performance deep spiking neural networks: a review of theories and methods.
Review in Frontiers in neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A Cascaded Quantized Spiking Neural Network for Real-Time ECG Arrhythmia Detection on Edge Hardware.Sensors (Basel, Switzerland) · 2026Article
- A Fire Detection Method Based on a Mind-Linked Continuous-Coupled Neural Network.Biomimetics (Basel, Switzerland) · 2026Article
- Speech Recognition with an fMRISNN Constrained by Human Functional Brain Networks: A Study of Enhanced MFCC-Driven Sparse Spike Encoding.Biomimetics (Basel, Switzerland) · 2026Article
- SPCNNet: spiking point cloud neural network for morphological neuron classification.Scientific reports · 2026Article
- Adaptive and lightweight surrogate gradients: enhancing training efficiency of spiking neural networks.Frontiers in neuroscience · 2026Article
- Development of digital hardware for a spiking image recognition network employing a novel burst-based reinforcement learning approach.Scientific reports · 2025Article
- Topology optimization of random memristors for input-aware dynamic SNN.Science advances · 2025Article
- An accurate and fast learning approach in the biologically spiking neural network.Scientific reports · 2025Article
- Bridging neuromorphic computing and deep learning for next-generation neural data interpretation.Frontiers in computational neuroscience · 2025Article
- Bidirectional dynamic threshold SNN for enhanced object detection with rich spike information.Frontiers in neuroscience · 2025Article
- NeuroMorphFusion: A Neuro-Inspired Hybrid Learning Framework for Interpretable Deep Lesion Detection in IoT-Enabled Healthcare Systems.Technology in cancer research & treatmentArticle
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Authors and funding
10 authors.
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Abstract
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal dynamics, and event-driven computation. The direct training algorithms based on the surrogate gradient method provide sufficient flexibility to design novel SNN architectures and explore the spatial-temporal dynamics of SNNs. According to previous studies, the performance of models is highly dependent on their sizes. Recently, direct training deep SNNs have achieved great progress on both neuromorphic datasets and large-scale static datasets. Notably, transformer-based SNNs show comparable performance with their ANN counterparts. In this paper, we provide a new perspective to summarize the theories and methods for training deep SNNs with high performance in a systematic and comprehensive way, including theory fundamentals, spiking neuron models, advanced SNN models and residual architectures, software frameworks and neuromorphic hardware, applications, and future trends.
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