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The encoding/decoding model of communication emerged in rough and general form in 1948 in claude e [18][19][20] these results do not provide an algorithm for performing the decoding. Shannon 's a mathematical theory of communication, where it was part of a technical schema for designating the technological encoding of signals
Gradually, it was adapted by communications scholars, most notably wilbur schramm, in the 1950s, primarily to explain how mass communications could. It consists of five chapters (information theory perspective of polar codes and polar encoding, decoding of polar codes, polarization of binary erasure channels, mathematical modelling of polar codes, channel combining and splitting and polarization rate and performance of polar codes), giving an introductory but mathematically detailed account. Neural coding (or neural representation) refers to the relationship between a stimulus and its respective neuronal responses, and the signalling relationships among networks of neurons in an ensemble
[1][2] action potentials, which act as the primary carrier of information in biological neural networks, are generally uniform regardless of the type of stimulus or the specific type of neuron.
Coding theory is the study of the properties of codes and their respective fitness for specific applications Codes are used for data compression, cryptography, error detection and correction, data transmission and data storage. Decoding methods in coding theory, decoding is the process of translating received messages into codewords of a given code There have been many common methods of mapping messages to codewords
These are often used to recover messages sent over a noisy channel, such as a binary symmetric channel. 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 The sliding application represents the 'convolution' of the encoder over the data, which gives rise to the term 'convolutional coding'
Coding theory is one of the most important and direct applications of information theory
It can be subdivided into source coding theory and channel coding theory Using a statistical description for data, information theory quantifies the number of bits needed to describe the data, which is the information entropy of the source. Decoding, in semiotics, is the process of interpreting a message sent by an addresser (sender) to an addressee (receiver)
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