Multitask training makes recurrent networks more modular and brain-like
For AI researchers, training one recurrent network on several cognitive tasks can produce a more modular system without hand-designed modules. In the study, multitask training increased modularity over single-task training, especially when the network’s capacity was strained; incremental multitask learning performed best while retaining superior task performance. The resulting structure more closely resembled biological brain networks than networks shaped only by spatial wiring costs.