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  2. Neural backpropagation - Wikipedia

    en.wikipedia.org/wiki/Neural_backpropagation

    Neural backpropagation is the phenomenon in which, after the action potential of a neuron creates a voltage spike down the axon (normal propagation), another impulse is generated from the soma and propagates towards the apical portions of the dendritic arbor or dendrites (from which much of the original input current originated).

  3. Feedforward neural network - Wikipedia

    en.wikipedia.org/wiki/Feedforward_neural_network

    In 1970, Seppo Linnainmaa published the modern form of backpropagation in his master thesis (1970). [23] [24] [13] G.M. Ostrovski et al. republished it in 1971. [25] [26] Paul Werbos applied backpropagation to neural networks in 1982 [7] [27] (his 1974 PhD thesis, reprinted in a 1994 book, [28] did not yet describe the algorithm [26]).

  4. Almeida–Pineda recurrent backpropagation - Wikipedia

    en.wikipedia.org/wiki/Almeida–Pineda_recurrent...

    Almeida–Pineda recurrent backpropagation is an extension to the backpropagation algorithm that is applicable to recurrent neural networks. It is a type of supervised learning . It was described somewhat cryptically in Richard Feynman 's senior thesis, and rediscovered independently in the context of artificial neural networks by both Fernando ...

  5. Delta rule - Wikipedia

    en.wikipedia.org/wiki/Delta_rule

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  6. Mathematics of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/Mathematics_of_artificial...

    Backpropagation training algorithms fall into three categories: steepest descent (with variable learning rate and momentum, resilient backpropagation); quasi-Newton (Broyden–Fletcher–Goldfarb–Shanno, one step secant);

  7. Backpropagation through time - Wikipedia

    en.wikipedia.org/wiki/Backpropagation_through_time

    Back_Propagation_Through_Time(a, y) // a[t] is the input at time t. y[t] is the output Unfold the network to contain k instances of f do until stopping criterion is met: x := the zero-magnitude vector // x is the current context for t from 0 to n − k do // t is time. n is the length of the training sequence Set the network inputs to x, a[t ...

  8. Backpropagation - Wikipedia

    en.wikipedia.org/wiki/Backpropagation

    In machine learning, backpropagation [1] is a gradient estimation method commonly used for training a neural network to compute its parameter updates. It is an efficient application of the chain rule to neural networks.

  9. Progress in Brain Research - Wikipedia

    en.wikipedia.org/wiki/Progress_in_Brain_Research

    Progress in Brain Research is a series of academic books on neuroscience published by Elsevier. The first volume appeared in 1963 and as of January 2014 [update] , 207 volumes have been published. The editors-in-chief of the series are Stephen Waxman ( Yale University School of Medicine ), Donald G. Stein ( Emory University ), Dick Swaab ...