Obtaining the gradient of what's known as the loss function is an essential step to establish the backpropagation algorithm developed by University of Michigan researchers to train a material.

Multi-layered neural architectures that implement learning require elaborate mechanisms for symmetric backpropagation of errors that are biologically implausible. Here the authors propose a simple ...

The backpropagation (BPN) algorithm is used in artificial neural networks (ANN) for training the networks that uses unsupervised learning and it is performed in two consecutive phases for the training ...

A Deep Neural Network (DNN) is a composite function of vector-valued functions, and in order to train a DNN, it is necessary to calculate the gradient of the loss function with respect to all ...

Neural networks using the backpropagation algorithm were biologically “unrealistic in almost every respect” he said. For one thing, neurons mostly send information in one direction.