# Long Short-term Memory with Two-Compartment Spiking Neuron

arXiv: https://arxiv.org/abs/2307.07231 · HTML: https://mlsanity.com/paper/2307.07231/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2023.

## Built on by (most cited first, 4 of 4)

- [Balanced Resonate-and-Fire Neurons](https://mlsanity.com/paper/2402.14603/) (2024)
- [Ternary Spike-based Neuromorphic Signal Processing System](https://mlsanity.com/paper/2407.05310/) (2024)
- [Global-Local Convolution with Spiking Neural Networks for Energy-efficient Keyword Spotting](https://mlsanity.com/paper/2406.13179/) (2024)
- [Understanding the Convergence in Balanced Resonate-and-Fire Neurons](https://mlsanity.com/paper/2406.00389/) (2024)

## Abstract (arXiv)

The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a result, it remains a challenging task for state-of-the-art spiking neural networks (SNNs) to identify long-term temporal dependencies since bridging the temporal gap necessitates an extended memory capacity. To address this challenge, we propose a novel biologically inspired Long Short-Term Memory Leaky Integrate-and-Fire spiking neuron model, dubbed LSTM-LIF. Our model incorporates carefully designed somatic and dendritic compartments that are tailored to retain short- and long-term memories. The theoretical analysis further confirms its effectiveness in addressing the notorious vanishing gradient problem. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, strong network generalizability, and high energy efficiency of the proposed LSTM-LIF model. This work, therefore, opens up a myriad of opportunities for resolving challenging temporal processing tasks on emerging neuromorphic computing machines.