ArticleNeural computation2018
SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.
Article in Neural computation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers.
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Who cites it
106 citing papers in PubMed.
- Temporal prediction captures retinal spiking responses across animal species.Nature communications · 2026Article
- Article
- Event-Based Vision at the Edge: A Review.Brain sciences · 2026Review
- GGAR: gradient guided adaptive regularization enhances deep learning classification of brassica species using codon usage bias.BMC bioinformatics · 2026Article
- Backpropagation-free spiking neural networks with the forward-forward algorithm.Scientific reports · 2026Article
- Model-agnostic linear-memory online learning in spiking neural networks.Nature communications · 2026Article
- Three factor delay learning rules for spiking neural networks.Frontiers in neuroscience · 2026Article
- Adaptive and lightweight surrogate gradients: enhancing training efficiency of spiking neural networks.Frontiers in neuroscience · 2026Article
- Interactions between long- and short-term synaptic plasticity transform temporal neural representations into spatial.Proceedings of the National Academy of Sciences of the United States of America · 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
- DelGrad: exact event-based gradients for training delays and weights on spiking neuromorphic hardware.Nature communications · 2025Article
- Neuromorphic computing for robotic vision: algorithms to hardware advances.Communications engineering · 2025Review
- Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks.Scientific reports · 2025Article
- Biologically-informed excitatory and inhibitory ratio for robust spiking neural network training.Scientific reports · 2025Article
- Spiking Neural Network Models of Interaural Time Difference Extraction via a Massively Collaborative Process.eNeuro · 2025Article
- SpyKing-Privacy-preserving framework for Spiking Neural Networks.Frontiers in neuroscience · 2025Article
- Astrocyte-gated multi-timescale plasticity for online continual learning in deep spiking neural networks.Frontiers in neuroscience · 2025Article
- Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network.Frontiers in neuroinformatics · 2025Article
- Brain-inspired learning rules for spiking neural network-based control: a tutorial.Biomedical engineering letters · 2025Review
46 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.
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Registered trials
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.