Modeling self-developing biological neural networks

Recent progress in chips–neuron interface suggests real biological neurons as long-term alternatives to silicon transistors. The first step ning such computing systems is to build an abstract model of self-assembled biological neural networks, much like computer ts manipulate abstract models of transistors. In this article, we propose a model of the structure of biological neural networks. Our model reproduces most of the graph properties exhibited by Caenorhabditis elegans, including its small-world structure and allows generating surrogate networks with realistic biological structure, as would be needed for complex information processing/computing tasks. r 2007 Elsevier B.V. All rights reserved.

• Type: Journal
• Year: 2007
• Where: IJON
• Authors: Hugues Berry, Olivier Temam
• Keywords: Abstract Models, Biological Neural Networks, IJON 2007, Realistic Biological Structure