Executive Functions and Constructive Neural Networks
Author: John Larry Stricker
Publisher:
Published: 2004
Total Pages: 268
ISBN-13:
DOWNLOAD EBOOKThe present work explores how executive functions can be implemented in neural networks. Computational models such as neural networks allow researchers to develop more sophisticated conceptualizations of how executive functioning could be implemented in the brain. However, most computational models are designed only to solve a single problem rather than to solve multiple problems and integrate new and old knowledge. The problem domain for the models of the present work consists of Boolean logic expressions. These expressions easily lend themselves to implementation in neural networks while at the same time they can represent a range of problems that relate to executive functions, such as learning complementary vs. unrelated information. Network architectures and training regimes are developed that allow neural networks to solve multiple problems constructively while minimizing the impact of interference. The networks illustrate that the constructive learning of multiple problems does not require an executive controller, separate memory systems, or the constructive addition of learning resources.
