Abstract
Modern digital computers outperform humans in tasks based on precise and fast arithmetic operations. However, people are much better and faster than computers in solving complex perceptual problems, such as recognizing images, often in the presence of disturbances. Also, humans can perform complex movements with precision and grace, even in the presence of disturbances, and can generalize from past experience. In a computer, usually there exists a single processor implementing a sequence of arithmetic and logical operations, nowadays at speeds approaching billion operations per second. However this type of devices has ability neither to adapt their structure nor to learn in the way that human being does. An Artificial Neural Model (ANM) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel mathematical structure of the information processing system. In this paper we focus on the mathematical modeling aspects of the basic unit of the human nervous system and Artificial Neural Model.
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