Coleman

Traditional models of neural networks used in computer science and much artificial intelligence research are typically based on an understanding of the brain from many decades ago. In this paper we present an overview of the major known functional properties of natural neural networks and present the evidence for how these properties have been implicated in learning processes as well as their interesting computational properties. Introducing some of these functional properties into neural networks evolved to perform learning or adaptation tasks has resulted in better solutions and improved evolvability, and a common theme emerging across computational studies of these properties is self-organisation. It is thought that self-organizing principles play a critical role in the development and functioning of natural neural networks, and thus an interesting direction for future research is explicitly exploring the use of self-organizing systems, via the functional properties reviewed here, in the development of neural networks for AI systems.