70 citations · 71 across the 6 of their papers we have counts for
4 papers · 1 filter
Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning
Spyridon Chavlis, Panayiota Poirazi
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 na…
Leveraging dendritic properties to advance machine learning and neuro-inspired computing
Michalis Pagkalos, Roman Makarov, Panayiota Poirazi
The brain is a remarkably capable and efficient system. It can process and store huge amounts of noisy and unstructured information using minimal energy. In contrast, current artif…
Dendritic Self-Organizing Maps for Continual Learning
Kosmas Pinitas, Spyridon Chavlis, Panayiota Poirazi
Current deep learning architectures show remarkable performance when trained in large-scale, controlled datasets. However, the predictive ability of these architectures significant…
Artificial neural networks in action for an automated cell-type classification of biological neural networks
Eirini Troullinou, Grigorios Tsagkatakis, Spyridon Chavlis +5
Identification of different neuronal cell types is critical for understanding their contribution to brain functions. Yet, automated and reliable classification of neurons remains a…