  One goal of general intelligence is to learn novel information without
overwriting prior learning. The utility of learning without forgetting (CF) is
twofold: first, the system can return to previously learned tasks after
learning something new. In addition, bootstrapping previous knowledge may allow
for faster learning of a novel task. Previous approaches to CF and
bootstrapping are primarily based on modifying learning in the form of changing
weights to tune the model to the current task, overwriting previously tuned
weights from previous tasks.However, another critical factor that has been
largely overlooked is the initial network topology, or architecture. Here, we
argue that the topology of biological brains likely evolved certain features
that are designed to achieve this kind of informational conservation. In
particular, we consider that the highly conserved property of modularity may
offer a solution to weight-update learning methods that adheres to the learning
without catastrophic forgetting and bootstrapping constraints. Final
considerations are then made on how to combine these two learning objectives in
a dynamical, general learning system.
