Nishikawa Takashi, Lai Ying-Cheng, Hoppensteadt Frank C
Department of Mathematics, Center for Systems Science and Engineering Research, Arizona State University, Tempe, Arizona 85287, USA.
Phys Rev Lett. 2004 Mar 12;92(10):108101. doi: 10.1103/PhysRevLett.92.108101. Epub 2004 Mar 10.
Networks of coupled periodic oscillators (similar to the Kuramoto model) have been proposed as models of associative memory. However, error-free retrieval states of such oscillatory networks are typically unstable, resulting in a near zero capacity. This puts the networks at disadvantage as compared with the classical Hopfield network. Here we propose a simple remedy for this undesirable property and show rigorously that the error-free capacity of our oscillatory, associative-memory networks can be made as high as that of the Hopfield network. They can thus not only provide insights into the origin of biological memory, but can also be potentially useful for applications in information science and engineering.
耦合周期振荡器网络（类似于Kuramoto模型）已被提出作为联想记忆的模型。然而，这种振荡网络的无差错检索状态通常是不稳定的，导致容量几乎为零。与经典的Hopfield网络相比，这使得这些网络处于劣势。在这里，我们针对这种不良特性提出了一种简单的补救方法，并严格证明我们的振荡联想记忆网络的无差错容量可以与Hopfield网络一样高。因此，它们不仅可以为生物记忆的起源提供见解，还可能在信息科学和工程应用中有用。
