Artificial neural networks are very distant from actual neuroscience. Researchers try to make their nets more "brain-like" but it rarely results in better algorithms. In the end they just use whatever works, and that happens to involve a lot of algorithms which are "biologically implausible". I.e. things that the brain couldn't physically do even if it wanted to.

E.g. sharing learned weights between different parts of the net is extremely effective, but probably not possible in actual physical neurons. Even the main algorithm used to train NNs, backpropagation, is considered biologically implausible. Although a lot of theories exist as to how neurons could implement very similar algorithms.