A complementary systems theory of meta-learning

Abstract

Humans have the ability to quickly adapt to new tasks and generalise what they learned to improve the learning process itself. At the core of this capacity for meta-learning lies a difficult credit assignment problem. After a learning episode, the system needs to determine how to change the components of plasticity such that the next time a similar task is encountered, a better learning outcome can be arrived at more quickly. In machine learning, this problem is often approached by storing the entire learning trajectory and revisiting it in reverse-time order, a process that places large demands on memory and is non-local in time. Here, we propose that hippocampal replay enables meta-learning in the neocortex without storing the entire learning trajectory.