Noun
(computing) An error correction technique used in neural networks
(neurology) A phenomenon in which the action potential of a neuron creates a voltage spike both at the end of the axon, as normally, and also back through to the dendrites from which much of the original input current originated.
Source: en.wiktionary.orgBackpropagation and resurgence A key advance that came later was the backpropagation algorithm which effectively solved the exclusive-or problem, and more generally the problem of quickly training multi-layer neural networks (Werbos 1975). Source: Internet
They are stacks of information-transforming modules that “learn” by repeatedly computing a chain of what are known as gradients (something rarely taught in high school calculus), which are the backbone of a family of algorithms known as backpropagation. Source: Internet
Generalization of backpropagation with application to a recurrent gas market model. Source: Internet
Unlike traditional artificial neural networks, spiking neural networks don’t require neurons to fire in each backpropagation cycle of the algorithm, but, rather, only when what’s known as a neuron’s “membrane potential” crosses a specific threshold. Source: Internet
I have a question on the backpropagation in a simple neural network (I am trying to derive the derivative for the backpropagation). Source: Internet