Neural mechanisms of relational learning and fast knowledge reassembly in plastic neural networks

Abstract

Humans and animals have a striking ability to learn relationships between items in experience (such as stimuli, objects and events), enabling structured generalization and rapid assimilation of new information. A fundamental type of such relational learning is order learning, which enables transitive inference (if A > B and B > C, then A > C) and list linking (A > B > C and D > E > F rapidly ‘reassembled’ into A > B > C > D > E > F upon learning C > D). Despite longstanding study, a neurobiologically plausible mechanism for transitive inference and rapid reassembly of order knowledge has remained elusive. Here we report that neural networks endowed with neuromodulated synaptic plasticity (allowing for self-directed learning) and identified through artificial metalearning (learning-to-learn) are able to perform both transitive inference and list linking and, further, express behavioral patterns widely observed in humans and animals. Crucially, only networks that adopt an ‘active’ solution, in which items from past trials are reinstated in neural activity in recoded form, are capable of list linking. These results identify fully neural mechanisms for relational learning, and highlight a method for discovering such mechanisms.