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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).
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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.
A neural network, also called a neuronal network, is an interconnected population of neurons (typically containing multiple neural circuits). [1] Biological neural networks are studied to understand the organization and functioning of nervous systems .
The remainder of the ectoderm gives rise to the epidermis. The ability of the mesoderm to convert the overlying ectoderm into neural tissue is called neural induction. In the early embryo, the neural plate folds outwards to form the neural groove. Beginning in the future neck region, the neural folds of this groove close to create the neural tube.
Although not derivable from biology, the model allows for a simplified, immediately available dynamic, without being a trivial simplification. [64] The experimental support is weak, but the model is useful as a didactic tool to introduce dynamics of spike generation through phase plane analysis.
Paul John Werbos (born September 4, 1947) is an American social scientist and machine learning pioneer. He is best known for his 1974 dissertation, which first described the process of training artificial neural networks through backpropagation of errors. [1]
Neurons form complex biological neural networks through which nerve impulses (action potentials) travel. Neurons do not touch each other (except in the case of an electrical synapse through a gap junction); instead, neurons interact at close contact points called synapses. A neuron transports its information by way of an action potential.