# Deep Spiking Neural Networks with Resonate-and-Fire Neurons

arXiv: https://arxiv.org/abs/2109.08234 · HTML: https://mlsanity.com/paper/2109.08234/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 16 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Training Deep Spiking Neural Networks using Backpropagation](https://mlsanity.com/paper/1608.08782/) (2016)
- [Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures](https://mlsanity.com/paper/1903.06379/) (2019)
- [Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future](https://mlsanity.com/paper/2001.07092/) (2020)
- [RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks](https://mlsanity.com/paper/1702.06064/) (2017)
- [Coarse scale representation of spiking neural networks: backpropagation through spikes and application to neuromorphic hardware](https://mlsanity.com/paper/2007.06176/) (2020)
- [Modeling the respiratory Central Pattern Generator with resonate-and-fire Izhikevich-Neurons](https://mlsanity.com/paper/1811.11388/) (2018)

## Built on by (most cited first, 5 of 5)

- [Balanced Resonate-and-Fire Neurons](https://mlsanity.com/paper/2402.14603/) (2024)
- [Scaling Up Resonate-and-Fire Networks for Fast Deep Learning](https://mlsanity.com/paper/2504.00719/) (2025)
- [SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks](https://mlsanity.com/paper/2506.06374/) (2025)
- [Understanding the Convergence in Balanced Resonate-and-Fire Neurons](https://mlsanity.com/paper/2406.00389/) (2024)
- [ASecond-Order SpikingSSM for Wearables](https://mlsanity.com/paper/2510.14386/) (2025)

## 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.