Hebbian Learning

Hebbian learning, a biologically inspired learning rule where synaptic connections strengthen when pre- and post-synaptic neurons fire together, aims to create artificial neural networks that learn efficiently and in a biologically plausible manner. Current research focuses on applying Hebbian principles to various architectures, including spiking neural networks, Hopfield networks, and deep learning models, often incorporating modifications like contrastive learning or neuron-centric approaches to improve performance and address limitations such as catastrophic forgetting. This research is significant because it offers potential for more energy-efficient and robust AI systems, particularly in edge computing and continual learning scenarios, while also providing valuable insights into the learning mechanisms of the brain.