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Neurodynamics analysis of brain information transmission

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Abstract

This paper proposes a model of neural networks consisting of populations of perceptive neurons, inter-neurons, and motor neurons according to the theory of stochastic phase resetting dynamics. According to this model, the dynamical characteristics of neural networks are studied in three coupling cases, namely, series and parallel coupling, series coupling, and unilateral coupling. The results show that the indentified structure of neural networks enables the basic characteristics of neural information processing to be described in terms of the actions of both the optional motor and the reflected motor. The excitation of local neural networks is caused by the action of the optional motor. In particular, the excitation of the neural population caused by the action of the optional motor in the motor cortex is larger than that caused by the action of the reflected motor. This phenomenon indicates that there are more neurons participating in the neural information processing and the excited synchronization motion under the action of the optional motor.

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Authors and Affiliations

  1. Institute for Cognitive Neurodynamics, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, P. R. China

    Ru-bin Wang  (王如彬) & Zhi-kang Zhang  (张志康)

  2. Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, P. R. China

    Chi K. Tse

Authors
  1. Ru-bin Wang  (王如彬)
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  2. Zhi-kang Zhang  (张志康)
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  3. Chi K. Tse
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Corresponding author

Correspondence to Ru-bin Wang  (王如彬).

Additional information

Communicated by Li-qun CHEN

Project supported by the National Natural Science Foundation of China (Nos. 10872068, 10672057)

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Wang, Rb., Zhang, Zk. & Tse, C.K. Neurodynamics analysis of brain information transmission. Appl. Math. Mech.-Engl. Ed. 30, 1415–1428 (2009). https://doi.org/10.1007/s10483-009-1107-y

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  • DOI: https://doi.org/10.1007/s10483-009-1107-y

Key words

Chinese Library Classification

2000 Mathematics Subject Classification

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