Storing and retrieving data in a parallel distributed-memory system. Doctoral thesis
The storage and retrieval of patterns in a Hopfield-like Parallel Distributed Memory is investigated experimentally with a view toward increasing its storage capacity. The first two Chapters give an overview of distributed memories and, in particular, the Hopfield distributed memory. This dissertation then experimentally investigates new and untested methods to increase the storage capabilities of a Hopfield-like neural net. Increasing the storage capacity by using the continuous-valued Hopfield memory is explored in Chapter 3 and the impact on capacity of data representation is experimentally investigated in Chapter 4. New ways of storing data are then discussed (changing the interconnect strengths) including in Chapter 7 developing a new method called Modifying the Energy Contour or MEC. In addition, this Chapter also outlines how to increase error-tolerance through the use of noisy patterns. The Hopfield distributed memory is then contrasted to another intelligent memory subsystem based on more of a traditional computer technology. In Chapter 8 it is seen that traditional computer technology using data-parallel techniques has a greater storage efficiency than possible with current Hopfield-like distributed memories.
- Research Organization:
- Brown Univ., Providence, RI (USA). Center for Neural Science
- OSTI ID:
- 5545127
- Report Number(s):
- AD-A-185177/3/XAB
- Country of Publication:
- United States
- Language:
- English
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