Hebbian Learning Rule

Hebbian learning, a biologically inspired learning rule where synaptic connections strengthen when neurons fire together, is being actively investigated for its potential to create more biologically plausible and energy-efficient artificial neural networks. Current research focuses on adapting Hebbian principles within various architectures, including associative memory models, self-organizing maps, and spiking neural networks, often incorporating modifications like anti-Hebbian components or integrating them with other learning algorithms such as forward-forward learning. This renewed interest stems from the limitations of backpropagation and the desire for more efficient and robust learning algorithms, with implications for neuromorphic computing and a deeper understanding of biological learning mechanisms.