Modeling Self-Developing Biological Neural Networks

Hugues Berry 1 Olivier Temam 1
1 ALCHEMY - Architectures, Languages and Compilers to Harness the End of Moore Years
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France
Abstract : 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.
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Neurocomputing, Elsevier, 2007, 70 (16-18), pp.2723-2734. 〈10.1016/j.neucom.2006.06.013〉
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Hugues Berry, Olivier Temam. Modeling Self-Developing Biological Neural Networks. Neurocomputing, Elsevier, 2007, 70 (16-18), pp.2723-2734. 〈10.1016/j.neucom.2006.06.013〉. 〈inria-00149082〉

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