Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills
Fig 8
Comparing the retention and forgetting of networks from the two treatments.
P&CC networks, which are more modular, are better at retaining associations learned on a previous task (winter associations) while learning a new task (summer associations), better at learning new (summer) associations, and significantly better when measuring performance on both the associations for the original task (winter) and the new task (summer). Note that networks were evolved with five days per season, so the results during those first five days are the most informative regarding the evolutionary mitigation of catastrophic forgetting: we show additional days to reveal longer-term consequences of the evolved architectures. Solid lines show median performance and shaded areas indicate 95% bootstrapped confidence intervals of the median. The retention scores (left panel) are normalized relative to the original performance before training on the new task (an unnormalized version is provided as Supp. S6 Fig). During all performance measurements, learning was disabled to prevent such measurements from changing an individual’s known associations (Methods).