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Logo of bmcsysbioBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Systems Biology
 
BMC Syst Biol. 2012; 6: 23.
Published online Mar 31, 2012. doi:  10.1186/1752-0509-6-23
PMCID: PMC3359270
An optimally evolved connective ratio of neural networks that maximizes the occurrence of synchronized bursting behavior
Chao-Yi Dong1 and Kwang-Hyun Chocorresponding author2
1Department of Automatic Control, Inner Mongolia University of Technology, Huhhot 010080, People's Republic of China
2Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of Korea
corresponding authorCorresponding author.
Chao-Yi Dong: dongchaoyi/at/imut.edu.cn; Kwang-Hyun Cho: ckh/at/kaist.ac.kr
Received December 14, 2011; Accepted March 31, 2012.
Abstract
Background
Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors.
Results
In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR.
Conclusions
In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA.
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