  Artificial neural networks have diverged far from their early inspiration in
neurology. In spite of their technological and commercial success, they have
several shortcomings, most notably the need for a large number of training
examples and the resulting computation resources required for iterative
learning. Here we describe an approach to neurological network simulation, both
architectural and algorithmic, that adheres more closely to established
biological principles and overcomes some of the shortcomings of conventional
networks.
