Unsupervised learning for robust working memory
Fig 4
Sensitivity of homeostatic learning rule on learning parameters.
A-B: Dependence of final balance ratio Wexc/Winh on r0 (A) and α (B). After reaching the steady state, Wexc/Winh was averaged over the trials whose mean and standard deviation were shown as red curve and graded area. C-D: Evolution of Wexc/Winh over trials (top) and the activity after reaching the steady state (bottom) for lower r0 (C) and higher r0 (D) compared to that in Fig 3B and 3C. E: Sensitivity to learning speed α. For a faster learning rate, the homeostatic plasticity leads to the oscillation even for properly tuned r0, leading to a larger standard deviation (square in B) compared to a slower learning rate (circle in B corresponding to Fig 3B and 3C).