Typical methods to study cognitive function are to record the electrical activities of animal neurons during the training of animals performing behavioral tasks. A key problem is that they fail to record all the relevant neurons in the animal brain. To alleviate this problem, we develop an RNN-based Actor–Critic framework, which is trained through reinforcement learning (RL) to solve two tasks analogous to the monkeys’ decision-making tasks. The trained model is capable of reproducing some features of neural activities recorded from animal brain, or some behavior properties exhibited in animal experiments, suggesting that it can serve as a computational platform to explore other cognitive functions. Furthermore, we conduct behavioral experiments on our framework, trying to explore an open question in neuroscience: which episodic memory in the hippocampus should be selected to ultimately govern future decisions. We find that the retrieval of salient events sampled from episodic memories can effectively shorten deliberation time than common events in the decision-making process. The results indicate that salient events stored in the hippocampus could be prioritized to propagate reward information, and thus allow decision-makers to learn a strategy faster.

研究认知功能的典型方法是在训练执行行为任务的动物期间记录动物神经元的电活动。一个关键问题是它们无法记录动物大脑中所有相关的神经元。为了减轻这个问题，我们开发了基于RNN的Actor-Critic框架，该框架通过强化学习（RL）进行了培训，以解决类似于猴子的决策任务的两个任务。经过训练的模型能够重现从动物大脑记录的神经活动的某些特征，或者在动物实验中表现出的某些行为特性，这表明它可以作为探索其他认知功能的计算平台。此外，我们在框架上进行了行为实验，试图探索神经科学领域的一个开放性问题：应选择海马中的哪些情景记忆来最终决定未来的决策。我们发现，从情景记忆中提取的显着事件的检索可以比普通事件在决策过程中有效地缩短审议时间。结果表明，可以优先考虑存储在海马体中的显着事件来传播奖励信息，从而使决策者可以更快地学习策略。