Strongly Diluted Neural Networks with Correlation-Causing Stimuli
Yegao Xiao, Harald Englisch, K.L. Yao · Communications in Theoretical Physics · 1992
The strongly diluted neural networks are studied rigorously under correlation-causing stimuli for any time step with a Gaussian distribution of stabilities. For the second time step, the analytical formulas of retrieval qualities are derived by taking the correlation between random variables into consideration. Numerical results show that the correlation deteriorates the improvement of the retrieval quality by persistent stimuli in comparison with the case neglecting it, but it is, at least for , not strong enough to cause essential discrepancy between the retrieval qualities for the networks with non-correlation- and correlation-causing stimuli respectively.