# Long Short-Term Memory Spiking Networks and Their Applications

arXiv: https://arxiv.org/abs/2007.04779 · HTML: https://mlsanity.com/paper/2007.04779/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 16 of 16)

- [Spikformer: When Spiking Neural Network Meets Transformer](https://mlsanity.com/paper/2209.15425/) (2022)
- [A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware](https://mlsanity.com/paper/2107.03992/) (2021)
- [Event-Based Eye Tracking. AIS 2024 Challenge Survey](https://mlsanity.com/paper/2404.11770/) (2024)
- [Autonomous Driving with Spiking Neural Networks](https://mlsanity.com/paper/2405.19687/) (2024)
- [LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units](https://mlsanity.com/paper/2402.04882/) (2024)
- [Spiking Neural Networks for event-based action recognition: A new task to understand their advantage](https://mlsanity.com/paper/2209.14915/) (2022)
- [Language Modeling on a SpiNNaker 2 Neuromorphic Chip](https://mlsanity.com/paper/2312.09084/) (2023)
- [Attention-free Spikformer: Mixing Spike Sequences with Simple Linear Transforms](https://mlsanity.com/paper/2308.02557/) (2023)
- [GRSN: Gated Recurrent Spiking Neurons for POMDPs and MARL](https://mlsanity.com/paper/2404.15597/) (2024)
- [Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models](https://mlsanity.com/paper/2405.00433/) (2024)
- [FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning](https://mlsanity.com/paper/2504.01257/) (2025)
- [Training a General Spiking Neural Network with Improved Efficiency and Minimum Latency](https://mlsanity.com/paper/2401.10843/) (2024)
- [Fourier or Wavelet bases as counterpart self-attention in spikformer for efficient visual classification](https://mlsanity.com/paper/2403.18228/) (2024)
- [MC-QDSNN: Quantized Deep evolutionary SNN with Multi-Dendritic Compartment Neurons for Stress Detection using Physiological Signals](https://mlsanity.com/paper/2410.04992/) (2024)
- [Spiking Neural Network for Intra-cortical Brain Signal Decoding](https://mlsanity.com/paper/2504.09213/) (2025)
- [Neuromorphic Speech Enhancement with Dual-Branch Spiking Neural Networks](https://mlsanity.com/paper/2606.23761/) (2026)

## Abstract (arXiv)

Recent advances in event-based neuromorphic systems have resulted in significant interest in the use and development of spiking neural networks (SNNs). However, the non-differentiable nature of spiking neurons makes SNNs incompatible with conventional backpropagation techniques. In spite of the significant progress made in training conventional deep neural networks (DNNs), training methods for SNNs still remain relatively poorly understood. In this paper, we present a novel framework for training recurrent SNNs. Analogous to the benefits presented by recurrent neural networks (RNNs) in learning time series models within DNNs, we develop SNNs based on long short-term memory (LSTM) networks. We show that LSTM spiking networks learn the timing of the spikes and temporal dependencies. We also develop a methodology for error backpropagation within LSTM-based SNNs. The developed architecture and method for backpropagation within LSTM-based SNNs enable them to learn long-term dependencies with comparable results to conventional LSTMs.