Shannon versus semantic information processing in the brain

Article Type

Research Article

Publication Title

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Abstract

Claude Shannon insisted that attributing an interpretation or meaning to information will destroy its generality and limit its scope. According to him it is only the statistical nature of information that matters. Semantic information is on the other hand, meaning of the information. Pattern recognition plays a big role in neural signal processing. Patterns can be thought of encoded semantic information in neural signals. In neuroscience both types of information have been studied extensively, without ever mentioning the term “semantic information,” whereas “Shannon information” has become a household name among the neuroscientists, often even without the term “Shannon.” In fact, neural information in general is a combination of both. In this review we highlight Shannon information theoretical aspects and semantic information theoretical aspects in neural information processing. In fact, neural information in general, is a combination of both. It has been elaborated how an organized study of semantic processing of neural information, particularly from a time series data mining point of view, can aid our understanding of information processing in the brain. This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Algorithmic Development > Biological Data Mining Algorithmic Development > Structure Discovery.

DOI

10.1002/widm.1284

Publication Date

5-1-2019

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