Fig. 2
From: Probabilistic metaplasticity for continual learning with memristors in spiking networks
Evolution of task accuracies with sequential training on the split-MNIST benchmark. The x-axis shows the latest task the network has learned. We evaluate the performance of task n only after the network encounters it and denote the accuracies as 0 before that. (a) With no probabilistic metaplasticity, the network learns the current task well, but forgets the initial tasks after sequentially learning multiple tasks. (b) Probabilistic metaplasticity with low activity threshold leads to high rigidity in the network, so it remembers previous tasks but cannot learn the last task. (c) High activity threshold can lead to loss in previous task accuracies while the network remains plastic to learn the new tasks. (d) Optimized activity threshold balances plasticity and rigidity such that the network maintains high initial task accuracies while maintaining the ability to learn new tasks.
