Applications

Abstract

Neural networks are meant to interact with the natural environment, and information about the latter is usually gathered through very noisy but redundant sensory signals. On the other hand, in the control of effectors or actuators (muscles, motors, etc.) one often has to coordinate many mutually dependent and redundant signals. In both cases, neural networks can be used to implement a great number of implicitly or otherwise poorly defined transformations between the variables.