Abstract
There have been a number of recent papers on information theory and neural networks, especially in a perceptual system such as vision. Some of these approaches are examined, and their implications for neural network learning algorithms are considered. Existing supervised learning algorithms such as Back Propagation to minimize mean squared error can be viewed as attempting to minimize an upper bound on information loss. By making an assumption of noise either at the input or the output to the system, unsupervised learning algorithms such as those based on Hebbian (principal component analysing) or anti-Hebbian (decorrelating) approaches can also be viewed in a similar light. The optimization of information by the use of interneurons to decorrelate output units suggests a role for inhibitory interneurons and cortical loops in biological sensory systems.