Development of a computational model on the neural activity patterns of a visual working memory in a hierarchical feedforward Network

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Understanding the mechanism of information processing in the human brain remains a unique challenge because the nonlinear interactions between the neurons in the network are extremely complex and because controlling every relevant parameter during an experiment is difficult. Therefore, a simulation using simplified computational models may be an effective approach. In the present study, we developed a general model of neural networks that can simulate nonlinear activity patterns in the hierarchical structure of a neural network system. To test our model, we first examined whether our simulation could match the previously-observed nonlinear features of neural activity patterns. Next, we performed a psychophysics experiment for a simple visual working memory task to evaluate whether the model could predict the performance of human subjects. Our studies show that the model is capable of reproducing the relationship between memory load and performance and may contribute, in part, to our understanding of how the structure of neural circuits can determine the nonlinear neural activity patterns in the human brain.
Publisher
KOREAN PHYSICAL SOC
Issue Date
2015-11
Language
English
Article Type
Article
Citation

JOURNAL OF THE KOREAN PHYSICAL SOCIETY, v.67, no.10, pp.L1713 - L1718

ISSN
0374-4884
DOI
10.3938/jkps.67.1713
URI
http://hdl.handle.net/10203/205540
Appears in Collection
BiS-Journal Papers(저널논문)
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