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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. Backpropagation - Wikipedia

    en.wikipedia.org/wiki/Backpropagation

    The goal of any supervised learning algorithm is to find a function that best maps a set of inputs to their correct output. The motivation for backpropagation is to train a multi-layered neural network such that it can learn the appropriate internal representations to allow it to learn any arbitrary mapping of input to output.

  4. Delta rule - Wikipedia

    en.wikipedia.org/wiki/Delta_rule

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  5. Nerve conduction velocity - Wikipedia

    en.wikipedia.org/wiki/Nerve_conduction_velocity

    Saltatory conduction. In neuroscience, nerve conduction velocity (CV) is the speed at which an electrochemical impulse propagates down a neural pathway.Conduction velocities are affected by a wide array of factors, which include age, sex, and various medical conditions.

  6. Retrograde signaling - Wikipedia

    en.wikipedia.org/wiki/Retrograde_signaling

    In neuroscience, retrograde signaling (or retrograde neurotransmission) refers more specifically to the process by which a retrograde messenger, such as anandamide or nitric oxide, is released by a postsynaptic dendrite or cell body, and travels "backwards" across a chemical synapse to bind to the axon terminal of a presynaptic neuron.

  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. 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.

  9. Dendritic spike - Wikipedia

    en.wikipedia.org/wiki/Dendritic_spike

    In general, backward propagation serves to communicate output information to the postsynaptic membrane. [4] In many neurotransmitter-releasing neurons, backward propagation of dendritic spikes signals the release of neurotransmitters. [18] For example, Mitral cells seem to serve both as projection neurons and as local interneurons.