mlsanityData to 7 Oct 2026

Deep Spiking Neural Networks with Resonate-and-Fire Neurons

arXiv 2109.08234 ↗First posted 16 Sep 2021cs.NE cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

In this work, we explore a new Spiking Neural Network (SNN) formulation with Resonate-and-Fire (RAF) neurons (Izhikevich, 2001) trained with gradient descent via back-propagation. The RAF-SNN, while more biologically plausible, achieves performance comparable to or higher than conventional models in the Machine Learning literature across different network configurations, using similar or fewer parameters. Strikingly, the RAF-SNN proves robust against noise induced at testing/training time, under both static and dynamic conditions. Against CNN on MNIST, we show 25% higher absolute accuracy with N(0, 0.2) induced noise at testing time. Against LSTM on N-MNIST, we show 70% higher absolute accuracy with 20% induced noise at training time.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 16 Sep 2021.

Builds on

8 of 24 references.

References · 8

  1. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 308
  2. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 236
  3. 2016Training Deep Spiking Neural Networks using Backpropagationcited by 203
  4. 2019Enabling Spike-based Backpropagation for Training Deep Neural Network Architecturescited by 119
  5. 2020Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Futurecited by 83
  6. 2017RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networkscited by 8
  7. 2020Coarse scale representation of spiking neural networks: backpropagation through spikes and application to neuromorphic hardwarecited by 3
  8. 2018Modeling the respiratory Central Pattern Generator with resonate-and-fire Izhikevich-Neuronscited by 1

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2024Balanced Resonate-and-Fire Neuronscited by 12
  2. 2025Scaling Up Resonate-and-Fire Networks for Fast Deep Learningcited by 4
  3. 2025SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networkscited by 2
  4. 2024Understanding the Convergence in Balanced Resonate-and-Fire Neuronscited by 0
  5. 2025ASecond-Order SpikingSSM for Wearablescited by 0