Figure 1. Motivating simulation results.

(a) We consider a network attempting to retain previously learned information that is subject to ongoing synaptic changes due to synaptic fluctuations and compensatory plasticity. (b) Simulations performed in this study use an abstract, rate based neural network (described in section Motivating example). The rate of synaptic fluctuations is constant over time. By iteratively increasing the compensatory plasticity rate in steps we observe a ‘sweet-spot’ compensatory plasticity rate, which is lower than that of the synaptic fluctuations, and which best controls task error. (c) A snapshot of the simulation described in b, at the point where the rates of synaptic fluctuations and compensatory plasticity are matched. Even as task error fluctuates around a mean value, individual weights experience systematic changes.