One-shot learning of paired association navigation with biologically plausible schemas

After learning a multiple paired-association (MPA) navigation task, rodents can rapidly learn new cue–location associations after a single exposure, a phenomenon proposed to be facilitated by cognitive schemas. However, it remains unclear what schemas support this behavior and how they could be implemented in biological neural circuits. Here, we identify a set of schemas sufficient for gradual then one-shot paired-association navigation, and implement them in a fully neural agent using biologically plausible plasticity rules. The agent gradually acquires a low-dimensional spatial metric representation using a path-integration based temporal-difference error, enabling self-localization across different environments. It also contains a memory network that forms one-shot associations between sensory cues and goal locations, implemented using reward-modulated exploratory Hebbian (EH) plasticity. A third network performs vector subtraction between the agent’s current and goal location to compute goal-directed actions. Supplementing these schemas with an actor–critic enables one-shot navigation past obstacles. Lastly, learning a working-memory gating mechanism using the temporal-difference error enables one-shot learning even with brief cue and distractor presentation. The model reproduces key behavioral features of rodent learning in the MPA task and generates testable predictions about the neural mechanisms underlying schema-based one-shot learning. Significance Statement Schema-dependent learning is exemplified by better recall of a lecture when one already has a schema for understanding. A rodent navigation task showing a shift from slow to rapid learning has been proposed to reflect such schema use, yet how schemas are neurally implemented and enable faster learning is poorly understood. We clarify this gap in this computational study by describing several schemas and implement them as neural networks with biologically plausible learning rules. Composing them into an agent reproduces schema-dependent rodent learning behavior. Because schemas also shape self beliefs, clarifying their neural basis has implications for psychopathology. At the same time, understanding how biological schemas support rapid learning may inform advances in machine learning.