The :20Brain-state-in-a-box" Neural model is a gradient descent algorithm
Journal of Mathematical Psychology
Stability and optimization analyses of the generalized brain-state-in-a-box neural network model
Journal of Mathematical Psychology
Associative neural memories
On neural networks that design neural associative memories
IEEE Transactions on Neural Networks
A synthesis procedure for brain-state-in-a-box neural networks
IEEE Transactions on Neural Networks
Analysis and synthesis of associative memories based on brain-state-in-a-box neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Large-scale pattern storage and retrieval using generalized brain-state-in-a-box neural networks
IEEE Transactions on Neural Networks
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
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This article is concerned with the synthesis of the optimally performing GBSB (generalized brain-state-in-a-box) neural associative memory given a set of desired binary patterns to be stored as asymptotically stable equilibrium points. Based on some known qualitative properties and newly observed fundamental properties of the GBSB model, the synthesis problem is formulated as a constrained optimization problem. Next, we convert this problem into a quasi-convex optimization problem called GEVP (generalized eigenvalue problem). This conversion is particularly useful in practice, because GEVPs can be efficiently solved by recently developed interior point methods. Design examples are given to illustrate the proposed approach and to compare with existing synthesis methods.