ArticleFrontiers in neuroscience2020
Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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59 citing papers in PubMed.
- A highly energy-efficient multi-core neuromorphic architecture for training deep spiking neural networks.Nature communications · 2026Article
- Leaky-Integrate-Fire Neuron via Synthetic Antiferromagnetic Coupling and Spin-Orbit Torque.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Development of digital hardware for a spiking image recognition network employing a novel burst-based reinforcement learning approach.Scientific reports · 2025Article
- 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
- Learning and inference with correlated neural variability.PNAS nexus · 2025Article
- Efficient and robust temporal processing with neural oscillations modulated spiking neural networks.Nature communications · 2025Article
- Machine learning model optimization for flood susceptibility zonation over the Kosi megafan, Himalayan foreland basin, India.Scientific reports · 2025Article
- Neuromorphic computing for robotic vision: algorithms to hardware advances.Communications engineering · 2025Review
- Encrypted Spiking Neural Networks Based on Adaptive Differential Privacy Mechanism.Entropy (Basel, Switzerland) · 2025Article
- An accurate and fast learning approach in the biologically spiking neural network.Scientific reports · 2025Article
- Engineered biological neuronal networks as basic logic operators.Frontiers in computational neuroscience · 2025Article
- SpyKing-Privacy-preserving framework for Spiking Neural Networks.Frontiers in neuroscience · 2025Article
- Sensitivity of Spiking Neural Networks Due to Input Perturbation.Brain sciences · 2024Article
- Brain-Inspired Architecture for Spiking Neural Networks.Biomimetics (Basel, Switzerland) · 2024Article
- Memristive leaky integrate-and-fire neuron and learnable straight-through estimator in spiking neural networks.Cognitive neurodynamics · 2024Article
- Understanding the Resistive Switching Behaviors of Top Electrode (Au, Cu, and Al)-Dependent TiOACS omega · 2024Article
- Auto-Spikformer: Spikformer architecture search.Frontiers in neuroscience · 2024Article
- Fourier or Wavelet bases as counterpart self-attention in spikformer for efficient visual classification.Frontiers in neuroscience · 2024Article
- Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding.Frontiers in neuroscience · 2024Article
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5 authors.
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Abstract
Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing paradigm. However, the typical shallow SNN architectures have limited capacity for expressing complex representations while training deep SNNs using input spikes has not been successful so far. Diverse methods have been proposed to get around this issue such as converting off-the-shelf trained deep Artificial Neural Networks (ANNs) to SNNs. However, the ANN-SNN conversion scheme fails to capture the temporal dynamics of a spiking system. On the other hand, it is still a difficult problem to directly train deep SNNs using input spike events due to the discontinuous, non-differentiable nature of the spike generation function. To overcome this problem, we propose an approximate derivative method that accounts for the leaky behavior of LIF neurons. This method enables training deep convolutional SNNs directly (with input spike events) using spike-based backpropagation. Our experiments show the effectiveness of the proposed spike-based learning on deep networks (VGG and Residual architectures) by achieving the best classification accuracies in MNIST, SVHN, and CIFAR-10 datasets compared to other SNNs trained with a spike-based learning. Moreover, we analyze sparse event-based computations to demonstrate the efficacy of the proposed SNN training method for inference operation in the spiking domain.
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