# Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning

arXiv: https://arxiv.org/abs/2404.03708 · HTML: https://mlsanity.com/paper/2404.03708/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2024.

## Built on by (most cited first, 7 of 7)

- [Structured Knowledge Accumulation: The Principle of Entropic Least Action in Forward-Only Neural Learning](https://mlsanity.com/paper/2504.03214/) (2025)
- [Dendritic Convolution for Noise Image Recognition](https://mlsanity.com/paper/2511.18699/) (2025)
- [Memory-DD: A Low-Complexity Dendrite-Inspired Neuron for Temporal Prediction Tasks](https://mlsanity.com/paper/2512.04094/) (2025)
- [A Lightweight Brain-Inspired Machine Learning Framework for Coronary Angiography: Hybrid Neural Representation and Robust Learning Strategies](https://mlsanity.com/paper/2601.15865/) (2026)
- [CDRL: A Reinforcement Learning Framework Inspired by Cerebellar Circuits and Dendritic Computational Strategies](https://mlsanity.com/paper/2602.15367/) (2026)
- [Dendritic Neural Networks with Equilibrium Propagation](https://mlsanity.com/paper/2605.08135/) (2026)
- [Updating the standard neuron model in artificial neural networks](https://mlsanity.com/paper/2605.30370/) (2026)

## Abstract (arXiv)

Artificial neural networks (ANNs) are at the core of most Deep learning (DL) algorithms that successfully tackle complex problems like image recognition, autonomous driving, and natural language processing. However, unlike biological brains who tackle similar problems in a very efficient manner, DL algorithms require a large number of trainable parameters, making them energy-intensive and prone to overfitting. Here, we show that a new ANN architecture that incorporates the structured connectivity and restricted sampling properties of biological dendrites counteracts these limitations. We find that dendritic ANNs are more robust to overfitting and outperform traditional ANNs on several image classification tasks while using significantly fewer trainable parameters. These advantages are likely the result of a different learning strategy, whereby most of the nodes in dendritic ANNs respond to multiple classes, unlike classical ANNs that strive for class-specificity. Our findings suggest that the incorporation of dendritic properties can make learning in ANNs more precise, resilient, and parameter-efficient and shed new light on how biological features can impact the learning strategies of ANNs.