Effect of connectivity in an associative memory model
Journal of Computer and System Sciences
On the problem of spurious patterns in neural associative memory models
IEEE Transactions on Neural Networks
Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning
Neural Computation
Efficient continuous-time asymmetric hopfield networks for memory retrieval
Neural Computation
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The process of pattern retrieval in a Hopfield model in which a random antisymmetric component is added to the otherwise symmetric synaptic matrix is studied by computer simulations. The introduction of the antisymmetric component is found to increase the fraction of random inputs that converge to the memory states. However, the size of the basin of attraction of a memory state does not show any significant change when asymmetry is introduced in the synaptic matrix. We show that this is due to the fact that the spurious fixed points, which are destabilized by the introduction of asymmetry, have very small basins of attraction. The convergence time to spurious fixed-point attractors increases faster than that for the memory states as the asymmetry parameter is increased. The possibility of convergence to spurious fixed points is greatly reduced if a suitable upper limit is set for the convergence time. This prescription works better if the synaptic matrix has an antisymmetric component.