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- W7907410 abstract "AbstractComplex systems are widely studied in various fields of science. A neural network is one of the typical examples of the complex systems. Recent studies in neuroscience reported that the neural networks are dynamically self-organized by the spike-timing dependent synaptic plasticity (STDP). Although the neural networks change their structure using the STDP dynamically, neural networks are often analyzed in a static state. Thus, in this paper, we analyze neural network structure from a dynamical point of view. then, we show that the self-organized neural network to which the STDP learning rule is applied generates the small-world effect and randomness of the inter-spike intervals (ISIs) in the self-organized neural network increases as the small-world effect becomes higher.KeywordsNeural NetworkLearning RuleSynaptic WeightRegular NetworkNeural Network StructureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W7907410 date "2009-02-09" @default.
- W7907410 modified "2023-10-17" @default.
- W7907410 title "Self-Organized Neural Network Structure Depending on the STDP Learning Rules" @default.
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- W7907410 doi "https://doi.org/10.1007/978-3-540-85632-0_36" @default.
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