# Dendritic Self-Organizing Maps for Continual Learning

arXiv: https://arxiv.org/abs/2110.13611 · HTML: https://mlsanity.com/paper/2110.13611/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [On Tiny Episodic Memories in Continual Learning](https://mlsanity.com/paper/1902.10486/) (2019)
- [Continuous Learning in Single-Incremental-Task Scenarios](https://mlsanity.com/paper/1806.08568/) (2018)
- [Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization](https://mlsanity.com/paper/1802.01569/) (2018)
- [Toward Understanding Catastrophic Forgetting in Continual Learning](https://mlsanity.com/paper/1908.01091/) (2019)
- [Class-Incremental Learning with Generative Classifiers](https://mlsanity.com/paper/2104.10093/) (2021)
- [Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks](https://mlsanity.com/paper/2106.07490/) (2021)
- [Continual Learning with Self-Organizing Maps](https://mlsanity.com/paper/1904.09330/) (2019)

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

- [Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps](https://mlsanity.com/paper/2402.12465/) (2024)
- [SatSOM: Saturation Self-Organizing Maps for Continual Learning](https://mlsanity.com/paper/2506.10680/) (2025)
- [On filter design in deep convolutional neural network](https://mlsanity.com/paper/2410.21644/) (2024)
- [Class Incremental Continual Learning with Self-Organizing Maps and Synthetic Replay](https://mlsanity.com/paper/2508.21240/) (2025)
- [Unsupervised Continual Learning with Growing Self-Organizing Maps and Synthetic Replay](https://mlsanity.com/paper/2608.27662/) (2026)

## Abstract (arXiv)

Current deep learning architectures show remarkable performance when trained in large-scale, controlled datasets. However, the predictive ability of these architectures significantly decreases when learning new classes incrementally. This is due to their inclination to forget the knowledge acquired from previously seen data, a phenomenon termed catastrophic-forgetting. On the other hand, Self-Organizing Maps (SOMs) can model the input space utilizing constrained k-means and thus maintain past knowledge. Here, we propose a novel algorithm inspired by biological neurons, termed Dendritic-Self-Organizing Map (DendSOM). DendSOM consists of a single layer of SOMs, which extract patterns from specific regions of the input space accompanied by a set of hit matrices, one per SOM, which estimate the association between units and labels. The best-matching unit of an input pattern is selected using the maximum cosine similarity rule, while the point-wise mutual information is employed for class inference. DendSOM performs unsupervised feature extraction as it does not use labels for targeted updating of the weights. It outperforms classical SOMs and several state-of-the-art continual learning algorithms on benchmark datasets, such as the Split-MNIST and Split-CIFAR-10. We propose that the incorporation of neuronal properties in SOMs may help remedy catastrophic forgetting.