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.
['Hongying Meng', 'Kofi Appiah', 'Andrew Hunter', 'Shigang Yue', 'Mervyn Hobden', 'Nigel Priestley', 'Peter Hobden', 'Cy Pettit']
A modified sparse distributed memory model for extracting clean patterns from noisy inputs