BMC Neuroscience

, 9:P15 | Cite as

Optimal neural connection mechanism in cortical network

  • Qingbai Zhao
  • Yi-Yuan Tang
Open Access
Poster presentation


Spatial Coverage Synaptic Connection Preferential Attachment Euclidean Plane Short Processing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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.



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.


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    Zhao QB, Feng HB, Tang Y: Modeling Human Cortical Network in Real Brain Space. Chin Phys Lett. 2007, 24 (12): 3582-3585. 10.1088/0256-307X/24/12/079.CrossRefGoogle Scholar

Copyright information

© Zhao and Tang; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd.

Authors and Affiliations

  1. 1.Institute of Neuroinformatics and Laboratory for Brain and MindDalian University of TechnologyDalianChina
  2. 2.Department of PsychologyUniversity of OregonEugeneUSA

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