The Content-Addressable Memory Problem

• Main Authors: Feng-Sheng Tsai, 蔡豐聲
• Other Authors: Mau-Shiang Shih
• Format: Others
• Language: en_US
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/86343578736626370368

碩士 === 國立臺灣師範大學 === 數學研究所 === 91 === Abstract. We propose a solution to a fundamental problem in neural nets : " Stored an arbitrary set of fundamental memories, does there exist a recursive network for which these fundamental memories are stable equilibrium states of the network ? " The heart of it is the conception of the emergent set, a Hamming star-convexity packing in the n-cube, the mathematical framework of Hebb's strengthened learning rule, and the CAM algorithm. We prove that the set of stable equilibrium states of the threshold network constructed by Hebb's strengthened learning rule that responds to incoming signals of the states of fundamental memories is the 01-span of the emergence of fundamental memories. On this basis, we reduce the question to a problem for constructing a threshold network with sparse connections that responds to incoming signals of the states of a generator of fundamental memories, and thereby probing the collective dynamics of the network. One of the great intellectual challenges is to nd the mechanism for storage of memory. The solution of the Content-Addressable Memory Problem indicates a mechanism for storage of memory that a network produced in the brains by sucking the kernel of the received stored memory items as incoming signals can correctly yield the entire memory items on the basis of sucient partial information by the chaotic dynamics with a regular strategy-set.