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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hu Lu Bao-Ming Li Hui Wei |
| Copyright Year | 2012 |
| Description | Author affiliation: Institutes of Brain Science, Fudan University, Shanghai, China (Bao-Ming Li) || School of Computer Science, Laboratory of Cognitive and Model Algorithm, Fudan University, Shanghai, China (Hu Lu) || Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, China (Hui Wei) |
| Abstract | Graph theory is a very useful tool in the study of functional and anatomical network in the brain. It had been widely used in the functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) signals. Only very few studies analyzed the neuronal connections composed of individually recorded neurons. Particularly applying in the case of neuronal functional networks of animal behavior-dependent was rare. Scientists have found the small-world network properties in the functional network derived from fMRI and EEG signals. Whether there are existing small-world properties in the neuronal networks of multi-electrode recording? We use graph theory techniques to construct and analyze the neuronal networks. In the functional networks of a simultaneously recorded population of neurons in prefrontal cortex of the rat, in a Y-maze working memory task, we find that the neuronal connection density is highly relevant to rat behavior. We find there is a small-world effect in the neuronal functional network compared to a random graph with the same size and average connection density. We also find that small-world properties have a great relationship to correlation coefficient threshold selection. These findings indicate that neuronal functional networks of multi-electrode recordings are also small-world networks. Network connection topology and connection density are related to the working memory tasks in the rat. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 1031829 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467314886 |
| ISSN | 21614393 |
| e-ISBN | 9781467314909 |
| e-ISBN | 9781467314893 |
| DOI | 10.1109/IJCNN.2012.6252572 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neurons Correlation Graph theory Electroencephalography Electrodes Educational institutions Brain modeling multi-electrode recording graph theory functional network small-world network Y-maze |
| Content Type | Text |
| Resource Type | Article |
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