Introduction to the theory of neural computation
Introduction to the theory of neural computation
Associative memory in a multimodular network
Neural Computation
Optimal connectivity in hardware-targetted MLP networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Connection strategies in associative memory models with spiking and non-spiking neurons
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
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In physical implementations of associative memory, wiring costs play a significant role in shaping patterns of connectivity. In this study of sparsely-connected associative memory, a range of architectures is explored in search of optimal connection strategies that maximize pattern-completion performance, while at the same time minimizing wiring costs. It is found that architectures in which the probability of connection between any two nodes is based on relatively tight Gaussian and exponential distributions perform well and that for optimum performance, the width of the Gaussian distribution should be made proportional to the number of connections per node. It is also established from a study of other connection strategies that distal connections are not necessary for good pattern-completion performance. Convergence times and network scalability are also addressed in this study.