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Zhuchun Li

As seen in: Wiley Online Library, Physical Review B
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Articles

Enhanced Error-Free Retrieval in Kuramoto-Type Associative-Memory Networks via Two-Memory Configuration

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By Zhuchun Li, Xiaoxue Zhao, Xiang Zhou
| Wiley Online Library Verified
Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement Data sharing is not applicable to this article as no data sets were generated or analyzed during the current study. References 1, “Network of Neural Oscillators for Retrieving Phase Information,” Physical Review Letters 74 (1995): 4075–4078. 2, “Phase Transitions of an Oscillator Neural Network With a Standard Hebb Learning Rule,” Physical Review E 58 (1998): 4865–4871.
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Unified approach for applications of oscillatory associative-memory networks with error-free retrieval

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By Xiaoxue Zhao, Xiaoping Xue, Zhuchun Li
| Physical Review B
Abstract Given a set of standard binary patterns and a defective pattern, the binary pattern retrieval task is to find the closest pattern to the defective one among these standard patterns. The associative-memory network of Kuramoto oscillators consisting of a Hebbian coupling term and a second-order Fourier term can be applied to this task.
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