A Neural Memory Architecture for Content As Well As Address-Based Storage & Recall: Theory and Applications

Chen, Chun-Hsien and Honavar, Vasant (1995) A Neural Memory Architecture for Content As Well As Address-Based Storage & Recall: Theory and Applications. Technical Report TR95-03, Department of Computer Science, Iowa State University.

Abstract

A Neural Memory Architecture for Content as well as
Address-Based Storage and Recall: Theory and Applications
Chun-Hsien Chen and Vasant Honavar
Artificial Intelligence Research Group
Department of Computer Science
226 Atanasoff Hall
Iowa State University
Ames, IA 50011-1040
chen@cs.iastate.edu, honavar@cs.iastate.edu
www: http://www.cs.iastate.edu/~honavar/aigroup.html
This paper presents an approach to design of a neural architecture for both
associative (content-addressed) and address-based memories.
Several interesting properties of the memory module are mathematically
analyzed in detail. When used as an associative memory, the proposed
neural memory module supports recall from partial input patterns,
(sequential) multiple recalls and fault tolerance. When used as an
address-based memory, the memory module can provide working space for
dynamic representations for symbol processing and shared message-passing
among neural network modules within an integrated neural network system.
It also provides for real-time update of memory contents by
one-shot learning without interference with other stored patterns.


Subjects:
All uncategorized technical reports


ID code:
00000094
Deposited by:
Staff Account on 16 February 1995





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