Abstract
In this paper, we explore features of neural network dynamics that were identified in simulation approaches with highly complex models (representing large populations of coupled oscillators) on the one hand, or basic discrete excitable models, on the other. Both types of modeling approaches could produce features such as irregular sustained network activity or modular functional connectivity. This observation poses the question, what are the essential model features that lead to characteristic phenomena of neural network dynamics?
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References
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Acknowledgments
We thank Gaurang Mahajan for contributing numerical simulations.
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© 2013 Springer Science+Business Media Dordrecht
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Hilgetag, C.C., Hütt, MT., Zhou, C. (2013). Model Complexity in the Study of Neural Network Phenomena. In: Yamaguchi, Y. (eds) Advances in Cognitive Neurodynamics (III). Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4792-0_11
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DOI: https://doi.org/10.1007/978-94-007-4792-0_11
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Online ISBN: 978-94-007-4792-0
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