By MIKE RANKER
Recent research demonstrates that brain-inspired shallow feedforward networks are capable of efficiently learning non-trivial classification tasks with less computational complexity, compared to deep learning architectures. This is in addition to previous experimental findings on sub-dendritic adaptation and anisotropic properties of neurons. Deep learning appears to be a key magical element




