A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation, the algorithm behind learning in AI. Every time a human or machine learns how ...

The learning algorithm that enables the runaway success of deep neural networks doesn’t work in biological brains, but researchers are finding alternatives that could. In 2007, some of the leading ...

The approximate logic neuron model (ALNM) is a single neural model with a dynamic dendritic structure. The ALNM uses a neural pruning function to eliminate unnecessary dendrite branches and synapses ...

A new technical paper titled “Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks” was published by researchers at ...

Five DECADES of research into artificial neural networks have earned Geoffrey Hinton the moniker of the Godfather of artificial intelligence (AI). Work by his group at the University of Toronto laid ...

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 ...

This is somewhat true for the neural network back-propagation algorithm. A major hurdle for many software engineers when trying to understand back-propagation, is the Greek alphabet soup of symbols ...

Back-propagation is the most common algorithm used to train neural networks. There are many ways that back-propagation can be implemented. This article presents a code implementation, using C#, which ...