Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills
Fig 6
PA networks are visually non-modular whereas P&CC networks tend to create a separate module for learning (red and orange neurons), as hypothesized in Fig. 1 (bottom).
Dark blue nodes are inputs that encode which type of food has been encountered. Light blue nodes indicate internal, non-modulatory neurons. Red nodes are reward or punishment inputs that indicate if a nutritious or poisonous item has been eaten. Orange neurons are neuromodulatory neurons that regulate learning. P&CC networks tend to separate the reward/punishment inputs and neuromodulatory neurons into a separate module that applies learning to downstream neurons that determine which actions to take. For each treatment, the highest-performing network from each of the nine highest-performing evolution experiments are shown (all are shown in the Supporting Information). In each panel, the left number reports performance and the right number reports modularity. We follow the convention from [23] of placing nodes in the way that minimizes the total connection length.