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Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning
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.
Dendritic Self-Organizing Maps for Continual Learning
Current deep learning architectures show remarkable performance when trained in large-scale, controlled datasets.
Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks
This article highlights specific features of biological neurons and their dendritic trees, whose adoption may help advance artificial neural networks used in various machine learning applications.
Artificial neural networks in action for an automated cell-type classification of biological neural networks
In this work we establish the first, automated cell-type classification method that relies on neuronal activity rather than molecular or cellular features.
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