On stability and solvability (or, when does a neural network solve a problem?)
Abstract
The importance of the Stability Problem in neurocomputing is discussed, as well as the need for the study of infinite networks. Stability must be the key ingredient in the solution of a problem by a neural network without external intervention. Infinite discrete networks seem to be the proper objects of study for a theory of neural computability which aims at characterizing problems solvable, in principle, by a neural network. Precise definitions of such problems and their solutions are given. Some consequences are explored, in particular, the neural unsolvability of the Stability Problem for neural networks. © 1992 Kluwer Academic Publishers.
Publication Title
Minds and Machines
Recommended Citation
Franklin, S., & Garzon, M. (1992). On stability and solvability (or, when does a neural network solve a problem?). Minds and Machines, 2 (1), 71-83. https://doi.org/10.1007/BF00261290
