  Artificial neural networks (ANNs) have emerged as an essential tool in
machine learning, achieving remarkable success across diverse domains,
including image and speech generation, game playing, and robotics. However,
there exist fundamental differences between ANNs' operating mechanisms and
those of the biological brain, particularly concerning learning processes. This
paper presents a comprehensive review of current brain-inspired learning
representations in artificial neural networks. We investigate the integration
of more biologically plausible mechanisms, such as synaptic plasticity, to
enhance these networks' capabilities. Moreover, we delve into the potential
advantages and challenges accompanying this approach. Ultimately, we pinpoint
promising avenues for future research in this rapidly advancing field, which
could bring us closer to understanding the essence of intelligence.
