Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning
This addresses the issue of energy-intensive and overfitting-prone deep learning for AI practitioners, though it is incremental as it builds on existing ANN frameworks with biological insights.
The paper tackles the problem of artificial neural networks (ANNs) being parameter-inefficient and prone to overfitting by introducing a new architecture inspired by biological dendrites, resulting in improved robustness and performance on image classification tasks with significantly fewer parameters.
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.