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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.
Much of the early work that applied a predictive coding framework to neural mechanisms came from sensory processing, particularly in the visual cortex. [ 3 ] [ 12 ] These theories assume that the cortical architecture can be divided into hierarchically stacked levels, which correspond to different cortical regions.
This neural coding and decoding loop is a symbiotic relationship and the crux of the brain's learning algorithm. Furthermore, the processes that underlie neural decoding and encoding are very tightly coupled and may lead to varying levels of representative ability.
Neuronal code or the 'language' that neuronal ensembles speak is very far from being understood. Currently, there are two main theories about neuronal code. 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 ...
One of the implications of the efficient coding hypothesis is that the neural coding depends upon the statistics of the sensory signals. These statistics are a function of not only the environment (e.g., the statistics of the natural environment), but also the organism's behavior (e.g., how it moves within that environment).
Neural computation is the information processing performed by networks of neurons. Neural computation is affiliated with the philosophical tradition known as Computational theory of mind , also referred to as computationalism, which advances the thesis that neural computation explains cognition .
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.
This so-called neural code is currently poorly understood and sensory neuroscience plays an important role in the attempt to decipher it. Looking at early sensory processing is advantageous since brain regions that are "higher up" (e.g. those involved in memory or emotion) contain neurons which encode more abstract representations.