Geometrical Perspective on Hairy Memory

Authors:
Cheng-Yuan Liou
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University, Republic of China
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Year:
2008

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Abstract

This paper constructs a balanced training used in the hairy network [1] to balance the vulnerable memory parts and improve the memory. It provides a perspective view on the geometrical structure of memory patterns. This training fixes many drawbacks of the Hopfield network, such as loading capacity, limit cycle and error tolerance.