Sparse distributed memory and related models
Associative neural memories
RAM-Based Neural Networks
Sparse Distributed Memory
Sparse distributed memory using N-of-M codes
Neural Networks
Kanerva's sparse distributed memory: an object-oriented implementation on the connection machine
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A new approach to Kanerva's sparse distributed memory
IEEE Transactions on Neural Networks
Coding static natural images using spiking event times: do neurons Cooperate?
IEEE Transactions on Neural Networks
Sparse Distributed Memory Using Rank-Order Neural Codes
IEEE Transactions on Neural Networks
Improving the performance of Kanerva's associate memory
IEEE Transactions on Neural Networks
Generalization and PAC learning: some new results for the class of generalized single-layer networks
IEEE Transactions on Neural Networks
Integer sparse distributed memory: Analysis and results
Neural Networks
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The Sparse Distributed Memory (SDM) proposed by Kanerva provides a simple model for human long-term memory, with a strong underlying mathematical theory. However, there are problematic features in the original SDM model that affect its efficiency and performance in real world applications and for hardware implementation. In this paper, we propose modifications to the SDM model that improve its efficiency and performance in pattern recall. First, the address matrix is built using training samples rather than random binary sequences. This improves the recall performance significantly. Second, the content matrix is modified using a simple tri-state logic rule. This reduces the storage requirements of the SDM and simplifies the implementation logic, making it suitable for hardware implementation. The modified model has been tested using pattern recall experiments. It is found that the modified model can recall clean patterns very well from noisy inputs.