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We show that two identical fully connected chaotic neural networks can always achieve a stochastic synchronization state when linked with a sufficiently large common noise. This is the case for both low-dimensional hyperchaos and high-dimensional spatiotemporal chaos. When the parameters of the two driven systems possess a tiny difference, weakly noise-induced synchronization is obtained. Unstable finite-precision synchronization of chaos with positive conditional Lyapunov exponent is also observed. It is caused by the on-off synchronizing intermittent dynamics.
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Shuai et al. (1998) studied this question.
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