Backpropagation

In machine learning, backpropagation of error (‘backprop’) is the algorithm most often used to train deep neural networks and is the most successful learning procedure for these networks.

Experiments show that these ion channels furnish the dendrites with a rich repertoire of electrical behaviors, from essentially passive responses, to subthreshold active responses, to active backpropagation of the action potential (AP) from the soma into the dendrites, to the initiation of APs in the dendritic tree.