Hebb postulated that, to store information in the brain, assemblies of excitatory neurons
coding for a percept are bound together via associative long-term synaptic plasticity. In this …

Spiking neural networks (SNNs) are gaining increasing attention as potential
computationally efficient alternatives to traditional artificial neural networks (ANNs) …

Traditional deep learning relies on end-to-end backpropagation for training, but it suffers
from drawbacks such as high memory consumption and not aligning with biological neural …

In this work, we tackle the problems of efficiency and scalability for predictive coding
networks in machine learning. To do so, we first propose a library called PCX, whose focus …

Current state-of-the-art deep networks are all powered by backpropagation. In this paper, we
explore alternatives to full backpropagation in the form of blockwise learning rules …

" Forward-only" algorithms, which train neural networks while avoiding a backward pass,
have recently gained attention as a way of solving the biologically unrealistic aspects of …

The Forward-Forward (FF) Algorithm has been recently proposed to alleviate the issues of
backpropagation (BP) commonly used to train deep neural networks. However, its current …

Spiking neural networks (SNNs), attributed to the binary, event-driven nature of spikes,
possess heightened biological plausibility and enhanced energy efficiency on neuromorphic …

Hebbian plasticity in winner-take-all (WTA) networks is highly attractive for neuromorphic on-
chip learning, owing to its efficient, local, unsupervised, and on-line nature. Moreover, its …

For a long time, biology and neuroscience fields have been a great source of inspiration for
computer scientists, towards the development of Artificial Intelligence (AI) technologies. This …