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- W2467804889 abstract "This chapter presents macroscopic properties of higher order neural networks. Randomly connected Neural Networks (RNNs) are known as a convenient model to investigate the macroscopic properties of neural networks. They are investigated by using the statistical method of neuro-dynamics. By applying the approach to higher order neural networks, macroscopic properties of them are made clear. The approach establishes: (a) there are differences between stability of RNNs and Randomly connected Higher Order Neural Networks (RHONNs) in the cases of the digital state -model and the analog state model; (b) there is no difference between stability of RNNs and RHONNs in the cases of the digital state -model and the analog state -model; (c) with neural networks with oscillation, there are large differences between RNNs and RHONNs in the cases of the digital state -model and the analog state -model, that is, there exists complex dynamics in each model for ; (d) behavior of groups composed of RHONNs are represented as a combination of the behavior of each RHONN." @default.
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- W2467804889 date "2013-01-01" @default.
- W2467804889 modified "2023-10-16" @default.
- W2467804889 title "On Some Dynamical Properties of Randomly Connected Higher Order Neural Networks" @default.
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- W2467804889 doi "https://doi.org/10.4018/978-1-4666-2175-6.ch016" @default.
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