Figure 5.

Robustness to spatial and temporal noise. (A) Noise was added by drawing the weight ω of the active state from a normal distribution. Three types of noise were tested: For spatial noise, a value ω ~ (ω; ω, σ²_ω) was assigned to each switch and maintained throughout the experiment. For temporal noise, the value ω was redrawn after every LTP transition. For spatial + temporal noise, the device specific spatial weight value determined the mean for redrawing ω after LTP transitions. (B–D) Number m_ki of active switches after learning at t = 5000 s. Shown are example networks for all three noise types. (E) Classification error after learning, based on 20 independently trained networks per noise type. Errorbars denote standard deviation among networks.