# Continuous learning of spiking networks trained with local rules

arXiv: https://arxiv.org/abs/2111.09553 · HTML: https://mlsanity.com/paper/2111.09553/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2021.

## Built on by (most cited first, 6 of 6)

- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [Replay4NCL: An Efficient Memory Replay-based Methodology for Neuromorphic Continual Learning in Embedded AI Systems](https://mlsanity.com/paper/2503.17061/) (2025)
- [Continual Learning with Columnar Spiking Neural Networks](https://mlsanity.com/paper/2506.17169/) (2025)
- [Online Continual Learning via Spiking Neural Networks with Sleep Enhanced Latent Replay](https://mlsanity.com/paper/2507.02901/) (2025)
- [Towards Improved Imbalance Robustness in Continual Multi-Label Learning with Dual Output Spiking Architecture (DOSA)](https://mlsanity.com/paper/2402.04596/) (2024)
- [Gradient-Free Continual Learning in Spiking Neural Networks via Inter-Spike Interval Regularization](https://mlsanity.com/paper/2604.16496/) (2026)

## Abstract (arXiv)

Artificial neural networks (ANNs) experience catastrophic forgetting (CF) during sequential learning. In contrast, the brain can learn continuously without any signs of catastrophic forgetting. Spiking neural networks (SNNs) are the next generation of ANNs with many features borrowed from biological neural networks. Thus, SNNs potentially promise better resilience to CF. In this paper, we study the susceptibility of SNNs to CF and test several biologically inspired methods for mitigating catastrophic forgetting. SNNs are trained with biologically plausible local training rules based on spike-timing-dependent plasticity (STDP). Local training prohibits the direct use of CF prevention methods based on gradients of a global loss function. We developed and tested the method to determine the importance of synapses (weights) based on stochastic Langevin dynamics without the need for the gradients. Several other methods of catastrophic forgetting prevention adapted from analog neural networks were tested as well. The experiments were performed on freely available datasets in the SpykeTorch environment.