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This article is part of the supplement: Seventeenth Annual Computational Neuroscience Meeting: CNS*2008

Open Access Poster presentation

Optimal neural connection mechanism in cortical network

Qingbai Zhao1 and Yi-Yuan Tang12*

Author Affiliations

1 Institute of Neuroinformatics and Laboratory for Brain and Mind, Dalian University of Technology, Dalian 116024, China

2 Department of Psychology, University of Oregon, Eugene, OR 97403, USA

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BMC Neuroscience 2008, 9(Suppl 1):P15  doi:10.1186/1471-2202-9-S1-P15


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/9/S1/P15


Published:11 July 2008

© 2008 Zhao and Tang; licensee BioMed Central Ltd.

Background

The mammalian cortical system consists of diverse neurons with different spatial coverage. Previous studies suggest that neurons with only local connections and those with most widespread connections should be most and least numerous, respectively [1]. However, synaptic connection mechanisms between neurons are still unclear. Based on the previous studies [1,2], we assume that neurons with large spatial coverage may be preferentially synaptically connected. We applied the methods of complex networks to model the neural connection mechanism. The cortical network was modeled in a two-dimensional Euclidean plane, in which neurons can only connect to those in their spatial coverage. For connection mechanisms, random connection and the preferential attachments to nearest neurons and to neurons with large spatial coverage were considered.

Results and conclusion

Results indicated that compared with other cases, the cortical network with the preferential attachment to neurons with large spatial coverage (PANLSC) showed an optimal architecture, represented by high clustering, short processing steps and short wiring lengths that are important statistical properties and constraints for the design of neural networks. We speculate that the PANLSC is an optimal neural connection mechanism in cortical systems.

Acknowledgements

This work was supported in part by National Natural Science Foundation of China Grant 60472017 and 30670699, Ministry of Education Grant NCET-06-0277 and 021010.

References

  1. Buzsáki G, Geisler C, Henze DA, Wang XJ: Interneuron Diversity series: Circuit complexity and axon wiring economy of cortical interneurons.

    Trends Neurosci 2004, 27:186-193. PubMed Abstract | Publisher Full Text OpenURL

  2. Zhao QB, Feng HB, Tang Y: Modeling Human Cortical Network in Real Brain Space.

    Chin Phys Lett 2007, 24(12):3582-3585. Publisher Full Text OpenURL