ArticleFrontiers in neuroscience2016
Training Deep Spiking Neural Networks Using Backpropagation.
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 146 papers.
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Who cites it
146 citing papers in PubMed.
- Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.npj biomedical innovations · 2026Review
- Temporal coding enables hyperacuity in event-based vision.Nature communications · 2026Article
- Backpropagation-free spiking neural networks with the forward-forward algorithm.Scientific reports · 2026Article
- Task success in trained spiking neural network models coincides with emergence of cross-stimulus-modulated inhibition.Biological cybernetics · 2026Article
- Energy-efficient traffic sign recognition using directly trained spiking neural networks and population decoding.Frontiers in neuroscience · 2026Article
- Exploring the spiking neural autoencoder: from hyperexcitability to noise-driven compensation.Frontiers in systems neuroscience · 2026Article
- Multi-plasticity synergy with adaptive mechanism assignment for training spiking neural networks.Frontiers in neuroscience · 2026Article
- A frugal Spiking Neural Network for unsupervised multivariate temporal pattern classification and multichannel spike sorting.Nature communications · 2025Article
- Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: A Review.Sensors (Basel, Switzerland) · 2025Review
- 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
- The road to commercial success for neuromorphic technologies.Nature communications · 2025Review
- Noise and Dynamical Synapses as Optimization Tools for Spiking Neural Networks.Entropy (Basel, Switzerland) · 2025Article
- Three-stage hybrid spiking neural networks fine-tuning for speech enhancement.Frontiers in neuroscience · 2025Article
- SpikeAtConv: an integrated spiking-convolutional attention architecture for energy-efficient neuromorphic vision processing.Frontiers in neuroscience · 2025Article
- Brain-inspired learning rules for spiking neural network-based control: a tutorial.Biomedical engineering letters · 2025Review
- SpyKing-Privacy-preserving framework for Spiking Neural Networks.Frontiers in neuroscience · 2025Article
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Signal-to-event encoding parameter selection for multiple event classification with spiking neural networks.Frontiers in neuroscience · 2025Article
- Adaptive Optimization and Dynamic Representation Method for Asynchronous Data Based on Regional Correlation Degree.Sensors (Basel, Switzerland) · 2024Article
86 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.