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Neural coding (or neural representation) is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the neuronal responses, and the relationship among the electrical activities of the neurons in the ensemble.
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
The neural encoding of sound is the representation of auditory sensation and perception in the nervous system. [1] The complexities of contemporary neuroscience are continually redefined. Thus what is known of the auditory system has been continually changing.
The rate encoding theory states that individual neurons encode behaviorally significant parameters by their average firing rates, and the precise time of the occurrences of neuronal spikes is not important. The temporal encoding theory, on the contrary, states that precise timing of neuronal spikes is an important encoding mechanism.
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Gain field encoding is a hypothesis about the internal storage and processing of limb motion in the brain. In the motor areas of the brain, there are neurons which collectively have the ability to store information regarding both limb positioning and velocity in relation to both the body (intrinsic) and the individual's external environment (extrinsic). [1]
Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
The first neural network takes as input the data points themselves, and outputs parameters for the variational distribution. As it maps from a known input space to the low-dimensional latent space, it is called the encoder. The decoder is the second neural network of this model.