  Schemas are knowledge structures that can enable rapid learning. Rodent
one-shot learning in a multiple paired association navigation task has been
postulated to be schema-dependent. But how schemas, conceptualized at Marr's
computational level, correspond with neural implementations remains poorly
understood, and a biologically plausible computational model of the rodent
learning has not been demonstrated. Here, we compose such an agent from schemas
with biologically plausible neural implementations. The agent contains an
associative memory that can form one-shot associations between sensory cues and
goal coordinates, implemented with a feedforward layer or a reservoir of
recurrently connected neurons whose plastic output weights are governed by a
novel 4-factor reward-modulated Exploratory Hebbian (EH) rule. Adding an
actor-critic allows the agent to succeed even if an obstacle prevents direct
heading. With the addition of working memory, the rodent behavior is
replicated. Temporal-difference learning of a working memory gating mechanism
enables one-shot learning despite distractors.
