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.

翻译：图式是一种能够促进快速学习的知识结构。啮齿类动物在多配对关联导航任务中的一次性学习被认为依赖于图式。然而，在马尔计算层面概念化的图式如何与神经实现相对应，目前仍缺乏清晰理解，且尚未有生物合理的啮齿类学习计算模型得到验证。本研究构建了一种基于生物合理神经实现的图式代理模型。该模型包含一个联想记忆模块，能够通过感觉线索与目标坐标之间的一次性关联进行学习，其实现方式为前馈层或采用受新颖四因子奖励调制的探索性赫布学习规则的循环连接神经元储层。通过引入行动者-评论家架构，即使在面临障碍物阻挡直接前进路径时，代理仍能成功完成任务。加入工作记忆后，该模型成功复现了啮齿类动物行为。基于时序差分学习的工作记忆门控机制，使得模型在面对干扰物时仍能实现一次性学习。