  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.
