  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. This
is achieved through the adoption of a different learning strategy, whereby most
of the nodes respond to several classes, unlike classical ANNs that strive for
class-specificity. These findings suggest that the incorporation of dendrites
can make learning in ANNs precise, resilient, and parameter-efficient and shed
new light on how biological features can impact the learning strategies of
ANNs.
