ePoster · Machine Learning
Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning
Spyridon Chavlis, Panayiota Poirazi · Bernstein Conference 2024
Artificial neural networks (ANNs) form the basis of most successful Deep Learning (DL) algorithms$^1$, which are capable of solving complex problems such as image recognition and natural language processing$^{2,3}$. However, unlike biological brains, which efficiently solve similar problems, DL algorithms require a large number of adjustable parameters, making them energy-intensive and susceptible to overfitting. In this study, a new ANN architecture is introduced that incorporates the structured connectivity and restricted sampling properties of biological dendrites, aiming to overcome these