@inproceedings{
    SiOr01b,
    editor = "Dorffner, G. and Bischof, H. and Hornik, K.",
    author = "{\v{S}}{\'{\i}}ma, Ji{\v{r}}{\'{\i}} and Orponen, Pekka",
    publisher = "Springer-Verlag",
    title = "Exponential Transients in Continuous-Time Symmetric {H}opfield Nets",
    url = "http://link.springer.de/link/service/series/0558/papers/2130/21300806.pdf",
    series = "Lecture Notes in Computer Science",
    booktitle = "Proceedings of Artificial Neural Networks -- ICANN 2001 (Vienna, August 2001)",
    address = "Berlin Heidelberg",
    abstract = "We establish a fundamental result in the theory of continuous-time neural computation, by showing that so called continuous-time symmetric Hopfield nets, whose asymptotic convergence is always guaranteed by the existence of a Liapunov function may, in the worst case, possess a transient period that is exponential in the network size. The result stands in contrast to e.g. the use of such network models in combinatorial optimization applications.",
    volume = "2130",
    flags = "public",
    year = "2001",
    keywords = "dynamical systems, continuous time, neural networks, Hopfield model, convergence time",
    pages = "806--813"
}