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

ABSTRACT: 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 match or outperform traditional ANNs on several image classification tasks while using significantly fewer trainable parameters. These advantages

SUBMITTER: Chavlis S 

PROVIDER: S-EPMC11754790 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature