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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).
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.
The biologically inspired Hodgkin–Huxley model of a spiking neuron was proposed in 1952. This model describes how action potentials are initiated and propagated. . Communication between neurons, which requires the exchange of chemical neurotransmitters in the synaptic gap, is described in various models, such as the integrate-and-fire model, FitzHugh–Nagumo model (1961–1962), and ...
Neural back-propagation is a method utilized by connectionist networks to show evidence of learning. After a connectionist network produces a response, the simulated results are compared to real-life situational results.
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.
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.
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 ...
In neuroscience, a biological neural network is a physical structure found in brains and complex nervous systems – a population of nerve cells connected by synapses. In machine learning , an artificial neural network is a mathematical model used to approximate nonlinear functions .