ArticleScientific reports2021
Event-based backpropagation can compute exact gradients for spiking neural networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Neuromorphic Devices and Computing for Sensing, Memory, and Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Federated training of spiking neural networks on edge hardware for audio processing.Frontiers in neuroscience · 2026Article
- Three factor delay learning rules for spiking neural networks.Frontiers in neuroscience · 2026Article
- Leveraging insights from neuroscience to build adaptive artificial intelligence.Nature neuroscience · 2026Review
- Sliding-window training on readout layer enhances liquid state machines for temporal prediction.Npj unconventional computing · 2026Article
- Backpropagation through space, time and the brain.Nature communications · 2025Article
- Ultrafast neural sampling with spiking nanolasers.Nature communications · 2025Article
- Efficient event-based delay learning in spiking neural networks.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
- DelGrad: exact event-based gradients for training delays and weights on spiking neuromorphic hardware.Nature communications · 2025Article
- The road to commercial success for neuromorphic technologies.Nature communications · 2025Review
- 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
- High-performance deep spiking neural networks with 0.3 spikes per neuron.Nature communications · 2024Article
- Spiking representation learning for associative memories.Frontiers in neuroscience · 2024Article
- Scalable network emulation on analog neuromorphic hardware.Frontiers in neuroscience · 2024Article
- Sparse-firing regularization methods for spiking neural networks with time-to-first-spike coding.Scientific reports · 2023Article
- Heterogeneity in Neuronal Dynamics Is Learned by Gradient Descent for Temporal Processing Tasks.Neural computation · 2023Article
- Phase relations of interneuronal activity relative to theta rhythm.Frontiers in neural circuits · 2023Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spiking neural networks combine analog computation with event-based communication using discrete spikes. While the impressive advances of deep learning are enabled by training non-spiking artificial neural networks using the backpropagation algorithm, applying this algorithm to spiking networks was previously hindered by the existence of discrete spike events and discontinuities. For the first time, this work derives the backpropagation algorithm for a continuous-time spiking neural network and a general loss function by applying the adjoint method together with the proper partial derivative jumps, allowing for backpropagation through discrete spike events without approximations. This algorithm, EventProp, backpropagates errors at spike times in order to compute the exact gradient in an event-based, temporally and spatially sparse fashion. We use gradients computed via EventProp to train networks on the Yin-Yang and MNIST datasets using either a spike time or voltage based loss function and report competitive performance. Our work supports the rigorous study of gradient-based learning algorithms in spiking neural networks and provides insights toward their implementation in novel brain-inspired hardware.
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