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A neural network model of spontaneous up and down transitions

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Abstract

Spontaneous periodic up and down transitions are considered to be a significant phenomenon that is characteristic of slow-wave sleep. We studied a neural network model of spontaneous up and down transitions based on our former study of a single-neuron model. We expanded the model by using two types of neurons—excitatory and inhibitory neurons—and redefining the connecting function between two neurons instead of assuming a constant connection, so that the model is closer to the in vivo network. Using this model, we studied the relationship between the transitions and network parameters such as size, structure and the ratio of excitatory to inhibitory neurons. We found that the network parameters have little impact on these spontaneous periodic up and down transitions. However, the intrinsic currents were found to play a leading role in the process. Then, we studied the transitions in the presence of stimulation and found that the addition of stimulation did have an effect on the network transitions. Through the observation and analysis of the findings, we have tried to explain the dynamics of up and down transitions and to lay the foundation for future work on the role of these transitions in cortex activity.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 11232005) and The Ministry of Education Doctoral Foundation (No. 20120074110020).

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Authors and Affiliations

  1. Institute for Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, 200237, People’s Republic of China

    Xuying Xu, Li Ni & Rubin Wang

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  1. Xuying Xu
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  2. Li Ni
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  3. Rubin Wang
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Correspondence to Rubin Wang.

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Xu, X., Ni, L. & Wang, R. A neural network model of spontaneous up and down transitions. Nonlinear Dyn 84, 1541–1551 (2016). https://doi.org/10.1007/s11071-015-2587-y

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  • DOI: https://doi.org/10.1007/s11071-015-2587-y

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