Six Principles for Biologically-Based Computational Models of Cortical Cognition (1998)
| Venue: | TRENDS IN COGNITIVE SCIENCES |
| Citations: | 43 - 14 self |
BibTeX
@INPROCEEDINGS{O'Reilly98sixprinciples,
author = {Randall C. O'Reilly},
title = {Six Principles for Biologically-Based Computational Models of Cortical Cognition},
booktitle = {TRENDS IN COGNITIVE SCIENCES},
year = {1998},
pages = {455--462},
publisher = {}
}
Years of Citing Articles
OpenURL
Abstract
This paper describes and motivates six principles for computational cognitive neuroscience models: biological realism, distributed representations, inhibitory competition, bidirectional activation propagation, errordriven task learning, and Hebbian model learning. Although these principles are supported by a number of cognitive, computational, and biological motivations, the prototypical neural network model (a feedforward backpropagation network) incorporates only two of them, and no widely used model incorporates all of them. This paper argues that these principles should be integrated into a coherent overall framework, and discusses some potential synergies and conflicts in doing so.







