Abstract
Continuous attractor neural networks with symmetric connection weights have been studied widely. However, there is short of results on continuous attractor neural networks with asymmetric connection weights. This paper studies the networks with asymmetric connection weights. To overcome the difficulties caused by asymmetric connection weights, an interesting new norm is proposed in a general vector space which is not Euclidean space. Then the new distance and attractivity are defined. Finally, the explicit expressions of continuous attractors of Cellular Neural Networks with asymmetric connection weights are obtained.
This work is supported by National Natural Science Foundation of China under Grant 61572112, 61103041, 61432012, 61803228, the Fundamental Research Funds for the Central Universities under Grant ZYGX2016J136.
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Yu, J., Yi, Z., Wang, C., Liao, Y., Pang, Z. (2018). Continuous Attractors of Nonlinear Neural Networks with Asymmetric Connection Weights. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_35
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