Neural networks and analog computation: beyond the Turing limit
Neural networks and analog computation: beyond the Turing limit
Computing with continuous-time Liapunov systems
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Recurrent Neural Networks: Design and Applications
Recurrent Neural Networks: Design and Applications
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
On the Computational Complexity of Binary and Analog Symmetric Hopfield Nets
Neural Computation
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We establish a fundamental result in the theory of continuous-time neural computation, by showing that so called continuoustime 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.