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Training of Spiking Neural Networks with Expectation-Propagation

arXiv 2506.23757 ↗First posted 30 Jun 2025cs.LG stat.ME stat.MLcited by 0

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Abstract (arXiv)

In this paper, we propose a unifying message-passing framework for training spiking neural networks (SNNs) using Expectation-Propagation. Our gradient-free method is capable of learning the marginal distributions of network parameters and simultaneously marginalizes nuisance parameters, such as the outputs of hidden layers. This framework allows for the first time, training of discrete and continuous weights, for deterministic and stochastic spiking networks, using batches of training samples. Although its convergence is not ensured, the algorithm converges in practice faster than gradient-based methods, without requiring a large number of passes through the training data. The classification and regression results presented pave the way for new efficient training methods for deep Bayesian networks.

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References · 20

  1. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  2. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  3. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 626
  4. 2015Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networkscited by 295
  5. 2018Deep Learning in Spiking Neural Networkscited by 263
  6. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 259
  7. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 245
  8. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 177
  9. 2013Expectation Propagation for approximate Bayesian inferencecited by 174
  10. 2018Lightweight Probabilistic Deep Networkscited by 77
  11. 2015Memory and information processing in neuromorphic systemscited by 48
  12. 2018Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networkscited by 27
8 more
  1. 2019An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applicationscited by 26
  2. 2015Stochastic Expectation Propagationcited by 22
  3. 2017Semi-analytical approximations to statistical moments of sigmoid and softmax mappings of normal variablescited by 20
  4. 2022Bayesian Continual Learning via Spiking Neural Networkscited by 13
  5. 2020BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learningcited by 8
  6. 2013Expectation Propagation for Neural Networks with Sparsity-promoting Priorscited by 7
  7. 2020VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuitscited by 7
  8. 2021Fast Scalable Image Restoration using Total Variation Priors and Expectation Propagationcited by 1

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