Geoffrey Hinton is a British computer scientist most noted for his work on the mathematics and applications of neural networks, and their relationship to information theory.

A simple introduction to Geoffrey Hinton's research can be found in his articles in Scientific American in September 1992 and October 1993. He investigates ways of using neural networks for learning, memory, perception and symbol processing and has over 200 publications in these areas. He was one of the researchers who introduced the back-propagation algorithm that has been widely used for practical applications. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural networks, mixtures of experts, Helmholtz machines and products of experts. His current main interest is in unsupervised learning procedures for neural networks with rich sensory input.

He is currently a professor in the computer science department at the University of Toronto.

External links

• Published papers (http://www.cs.toronto.edu/~hinton/chronological.html) (chronological)
• Homepage (http://www.cs.toronto.edu/~hinton/) (at UofT)
• Gatsby Computational Neuroscience Unit (http://www.gatsby.ucl.ac.uk/) (founding director)