ArticleFrontiers in neuroscience2018
Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 145 papers.
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145 citing papers in PubMed.
- Temporal coding enables hyperacuity in event-based vision.Nature communications · 2026Article
- Neuromorphic Cardiac Sensing: A Bio-Inspired Spiking Neural Network with Sensory-Adaptive Encoding for Energy-Efficient Arrhythmia Detection from ECG and PPG Signals.Biomimetics (Basel, Switzerland) · 2026Article
- SAD-SNN: Spatial-Activation Distillation for High-Performance Spiking Neural Networks.Sensors (Basel, Switzerland) · 2026Article
- A Cascaded Quantized Spiking Neural Network for Real-Time ECG Arrhythmia Detection on Edge Hardware.Sensors (Basel, Switzerland) · 2026Article
- SE-SNN: Squeeze-and-Excitation-Enhanced Spiking Neural Networks with Learnable Neuron Dynamics for Event-Based Vision.Biomimetics (Basel, Switzerland) · 2026Article
- Event-Based Vision at the Edge: A Review.Brain sciences · 2026Review
- Monolithic cell-on-memristor architecture enables wafer-scale integration of oscillatory chemoreceptors for bio-realistic gustatory chips.Nature materials · 2026Article
- Bioinspired spiking architecture enables energy constrained touch encoding.Nature communications · 2026Article
- Impact of Neuron Models on Spiking Neural Network Performance: A Complexity-based Classification Approach.Neuroinformatics · 2026Article
- Enhancing time scalability of spiking neural networks with dynamic time constants.Scientific reports · 2026Article
- Multi-plasticity synergy with adaptive mechanism assignment for training spiking neural networks.Frontiers in neuroscience · 2026Article
- Adaptive and lightweight surrogate gradients: enhancing training efficiency of spiking neural networks.Frontiers in neuroscience · 2026Article
- Energy-efficient traffic sign recognition using directly trained spiking neural networks and population decoding.Frontiers in neuroscience · 2026Article
- Article
- Microelectrode arrays cultured with in vitro neural networks for motion control tasks: encoding and decoding progress and advances.Microsystems & nanoengineering · 2025Review
- Neuromorphic computing paradigms enhance robustness through spiking neural networks.Nature communications · 2025Article
- BIASNN: a biologically inspired attention mechanism in spiking neural networks for image classification.Scientific reports · 2025Article
- A frugal Spiking Neural Network for unsupervised multivariate temporal pattern classification and multichannel spike sorting.Nature communications · 2025Article
- Learning and inference with correlated neural variability.PNAS nexus · 2025Article
- Temporal single spike coding for effective transfer learning in spiking neural networks.Scientific reports · 2025Article
85 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Spiking neural networks (SNNs) are promising in ascertaining brain-like behaviors since spikes are capable of encoding spatio-temporal information. Recent schemes, e.g., pre-training from artificial neural networks (ANNs) or direct training based on backpropagation (BP), make the high-performance supervised training of SNNs possible. However, these methods primarily fasten more attention on its spatial domain information, and the dynamics in temporal domain are attached less significance. Consequently, this might lead to the performance bottleneck, and scores of training techniques shall be additionally required. Another underlying problem is that the spike activity is naturally non-differentiable, raising more difficulties in supervised training of SNNs. In this paper, we propose a spatio-temporal backpropagation (STBP) algorithm for training high-performance SNNs. In order to solve the non-differentiable problem of SNNs, an approximated derivative for spike activity is proposed, being appropriate for gradient descent training. The STBP algorithm combines the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD), and does not require any additional complicated skill. We evaluate this method through adopting both the fully connected and convolutional architecture on the static MNIST dataset, a custom object detection dataset, and the dynamic N-MNIST dataset. Results bespeak that our approach achieves the best accuracy compared with existing state-of-the-art algorithms on spiking networks. This work provides a new perspective to investigate the high-performance SNNs for future brain-like computing paradigm with rich spatio-temporal dynamics.
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