Backpropagation relies on instantaneous weight transport and global updates, thus
questioning its neural plausibility. Continual learning mechanisms that are largely …

Movement of model parameters from memory to computing elements in deep learning (DL)
has led to a growing imbalance known as the memory wall. Neuromorphic computation-in …

Alternatives to backpropagation have long been studied to better understand how biological
brains may learn. Recently, they have also garnered interest as a way to train neural …

Backpropagation (BP), a foundational algorithm for training artificial neural networks,
predominates in contemporary deep learning. Although highly successful, it is often …

The field of neuromorphic engineering addresses the high energy demands of neural
networks through brain-inspired hardware for efficient neural network computing. For on …

Feedback alignment algorithms are an alternative to backpropagation to train neural
networks, whereby some of the partial derivatives that are required to compute the gradient …

This thesis explores the temporal structure of human memory through the use of deep neural
networks. The research aims to extend the study conducted by Roseboom et al. in" Activity in …