Neural networks are interconnections of simple units called

neurons, capable of computing complex functions. Hallmarks of a neural network are nonlinear response properties of single neurons, a high degree of interconnectivity between neurons, and adaptation and learning of connectivity and associated parameters. Neural networks are often divided into two general classes, biological and artificial, though this is less of a dichotomy than a spectrum. In the strictest sense, a biological neural network is a network comprising real neurons (i.e., constructed from living neural tissue) and found within a biological nervous system. Models of biological neural networks have been developed that abstract and simplify, at some level, biological realism in order to study a specific aspect of the network or reduce it to the hypothesized key elements underlying its behavior. These models are termed biologically based neural network models, though they do not capture all the complexity of the real biological system. As the level of abstraction increases, the tendency is toward less of a direct link with neurobiol-ogy. Artificial neural networks lie at the end of the spectrum at which mathematical description and compactness become more important than biological plausibility. Nonetheless, much of the research in the world of artificial neural networks has, at its origin, the study of biological neural networks. Computer models of both biological and artificial neural networks have been employed in the study of brain function, as well as in the development of ''brainlike'' systems

Encyclopedia of the Human Brain Volume 3

Copyright 2002, Elsevier Science (USA).

All rights reserved.

for prediction, recognition, and control in real-world environments.

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