Unsupervised learning for robust working memory
Fig 2
Recovery dynamics dependence on learning parameters under differential plasticity.
A: Phase-plane of activity r and synaptic strength of recurrent excitation Wexc. The small black arrows represent a vector field for the dynamics of r and Wexc, described in Eq 2. The red curve is a trajectory starting from 10% perturbation in Wexc, that is, Wexc = 0.9Winh with Winh = 500. During the stimulus presentation, the trajectory jumps horizontally, and input strengths vary randomly across trials. The big arrows indicate the effects of changing the learning speed α or Winh (blue vertical) and relative mean input strengths c (magenta horizontal). B-E: Dependence of recovery speed on learning and network parameters. The minimum number of trials for Wexc to reach up to 0.99Winh, that is, about 1% from perfect tuning was obtained by varying α (B), c (C), Winh (D), perturbation strength p (E). All parameters change from 50% to 200% of those used in Fig 1.