LFP functional network analysis of different states in hippocampus of pigeons

In this paper, combined with complex system theory, the synchronization likelihood algorithm was utilized to establish local field potential (LFP) functional network of hippocampus in three different states of pigeons, i.e. motion during daytime, static during daytime and static during night. The LFP signals and LFP functional network was qualitatively and quantitatively analyzed for each state. The results demonstrate that the LFP signal power in daytime motion state is obviously higher than the other two states. However, the clustering coefficient, average path length, global efficiency and transitivity in night static state are significantly higher than the other two states. Meanwhile, the average path length in daytime static state is significantly higher than that in daytime motion state. These results show that the neural activities are different for different states of pigeon. Moreover, compared with motion state, the information interaction and processing speed in static state may be faster.

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