Plausible Learning

Plausible learning in artificial neural networks aims to develop learning algorithms that mimic the biological mechanisms of the brain, addressing the limitations of backpropagation. Current research focuses on developing biologically-inspired alternatives such as forward-forward algorithms, feedback alignment, and STDP-based rules, often implemented in novel architectures like memory networks and spiking neural networks. This field is significant because biologically plausible learning offers potential advantages in energy efficiency, real-time adaptation, and improved understanding of neural computation, impacting both theoretical neuroscience and the development of more efficient and robust AI systems.