Psychology Dictionary of ArgumentsHome
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| Neural networks: Neural networks are computational models inspired by the human brain, designed to recognize patterns and solve complex problems. They consist of layers of interconnected nodes (analogous to neurons) that process input data and learn to perform tasks by adjusting the strength of connections based on feedback. Used extensively in machine learning, they enable applications like image recognition, language processing, and predictive analysis. See also Artificial Neural networks, Connectionism, Computer models, Computation, Artificial Intelligence, Machine learning._____________Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments. | |||
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Warren McCulloch on Neural Networks - Dictionary of Arguments
Norvig I 16 Neural networks/Artificial Intelligence/McCulloch/Pitts/Norvig: The first work that is now generally recognized as AI was done by Warren McCulloch and Walter Pitts (1943)(1). They drew on three sources: knowledge of the basic physiology and function of neurons in the brain; a formal analysis of propositional logic due to Russell and Whitehead; and Turing’s theory of computation. They proposed a model of artificial neurons in which each neuron is characterized as being “on” or “off,” with a switch to “on” occurring in response to stimulation by a sufficient number of neighboring neurons. The state of a neuron was conceived of as “factually equivalent to a proposition which proposed its adequate stimulus.” They showed, for example, that any computable function could be computed by some network of connected neurons, and that all the logical connectives (and, or, not, etc.) could be implemented by simple net structures. McCulloch and Pitts also suggested that suitably defined networks could learn. >Artificial Intelligence, >Strong Artificial Intelligence, >Artificial General Intelligence, >Artificial Neural Networks. 1. McCulloch, W. S. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–137 - - - Norvig I 731 Neural Networks/McCulloch/Pitts/Norvig/Russell: (McCulloch and Pitts, 1943)(1) were well aware that a single threshold unit would not solve all their problems. In fact, their paper proves that such a unit can represent the basic Boolean functions AND, OR, and NOT and then goes on to argue that any desired functionality can be obtained by connecting large numbers of units into (possibly recurrent) networks of arbitrary depth. The problem was that nobody knew how to train such networks. >Artificial Neural Networks/Norvig/Russell, >Neural networks/Norvig/Russell. 1. McCulloch, W. S. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–137._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition. |
McCulloch, John Ramsay Norvig I Peter Norvig Stuart J. Russell Artificial Intelligence: A Modern Approach Upper Saddle River, NJ 2010 |
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> Counter arguments against McCulloch
> Counter arguments in relation to Neural Networks
