Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills
Fig 1
Two hypotheses for how neural modularity can improve learning.
Hypothesis 1: Evolving non-modular networks leads to the forgetting of old skills as new skills are learned. Evolving networks with a pressure to minimize connection costs leads to modular solutions that can retain old skills as new skills are learned. Hypothesis 2: Evolving modular networks makes reward-based learning easier, because it allows a clear separation of reward signals and learned skills. We present evidence for both hypotheses in this paper.