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Modeling self-developing biological neural networks
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DOI：10.1016/j.neucom.2006.06.013.png)
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Recent progress in chips-neuron interface suggests real biological neurons as long-term alternatives to silicon transistors. The first step to designing such computing systems is to build an abstract model of self-assembled biological neural networks, much like computer architects 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. (c) 2007 Elsevier B.V. All rights reserved.
Keyword:
biological neural networks
network structure
growth model
graph properties
computing devices
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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