Biologically plausible learning mechanisms have implications for understanding brain functions and engineering intelligent systems. Inspired by the multi-scale recurrent connectivity in the brain, we ...

In a variety of forms, neural networks have seen an exponential rise in attention during the last decade. Neural networks trained with gradient descent are currently outperforming other, more ...

Resilient back propagation (Rprop), an algorithm that can be used to train a neural network, is similar to the more common (regular) back-propagation. But it has two main advantages over back ...

Artificial Neural Network (ANN) are highly interconnected and highly parallel systems. Back Propagation is a common method of training artificial neural networks so as to minimize objective function.

Training a neural network is the process of finding a set of weight and bias values so that for a given set of inputs, the outputs produced by the neural network are very close to some target values.