Sparse distributed memory and related models
Associative neural memories
Attractor Neural Networks with Hypercolumns
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Sparse distributed memory using N-of-M codes
Neural Networks
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Robot navigation and manipulation based on a predictive associative memory
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
A modified sparse distributed memory model for extracting clean patterns from noisy inputs
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Memory capacities for synaptic and structural plasticity
Neural Computation
Neural associative memory for brain modeling and information retrieval
Information Processing Letters - Special issue on applications of spiking neural networks
Neural associative memory with optimal bayesian learning
Neural Computation
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
The capacity of the Kanerva associative memory
IEEE Transactions on Information Theory
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Sparse distributed memory is an auto-associative memory system that stores high dimensional Boolean vectors. Here we present an extension of the original SDM, the Integer SDM that uses modular arithmetic integer vectors rather than binary vectors. This extension preserves many of the desirable properties of the original SDM: auto-associativity, content addressability, distributed storage, and robustness over noisy inputs. In addition, it improves the representation capabilities of the memory and is more robust over normalization. It can also be extended to support forgetting and reliable sequence storage. We performed several simulations that test the noise robustness property and capacity of the memory. Theoretical analyses of the memory's fidelity and capacity are also presented.