Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Introduction to Neural and Cognitive Modeling
Introduction to Neural and Cognitive Modeling
The Computational Brain
A neural-network learning theory and a polynomial time RBF algorithm
IEEE Transactions on Neural Networks
Cholangiocarcinoma--An Automated Preliminary Detection System Using MLP
Journal of Medical Systems
The design of an optimal decision-making algorithm for fertilization
Mathematical and Computer Modelling: An International Journal
GOFAM: a hybrid neural network classifier combining fuzzy ARTMAP and genetic algorithm
Artificial Intelligence Review
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This article points out some very serious misconceptions about the brain in connectionism and artificial neural networks. Some of the connectionist ideas have been shown to have logical flaws, while others are inconsistent with some commonly observed human learning processes and behavior. For example, the connectionist ideas have absolutely no provision for learning from stored information, something that humans do all the time. The article also argues that there is definitely a need for some new ideas about the internal mechanisms of the brain. It points out that a very convincing argument can be made for a "control theoretic" approach to understanding the brain. A "control theoretic" approach is actually used in all connectionist and neural network algorithms and it can also be justified from recent neurobiological evidence. A control theoretic approach proposes that there are subsystems within the brain that control other subsystems. Hence a similar approach can be taken in constructing learning algorithms and other intelligent systems.