利用强化学习算法解释人类脑对高维状态的抽象表示：how humans can map high-dimensional sensory inputs in actions

论文：
《Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments》
地址：
https://www.cell.com/neuron/fulltext/S0896-6273(20)30899-0
正文：
https://www.cell.com/neuron/pdf/S0896-6273(20)30899-0.pdf
补充信息：
https://www.cell.com/cms/10.1016/j.neuron.2020.11.021/attachment/57cc3979-b15e-468c-a4df-e8927360c70e/mmc1

In Brief
Cross et al. scanned humans playing Atari
games and utilized a deep reinforcement
learning algorithm as a model for how
humans can map high-dimensional
sensory inputs in actions.
Representations in the intermediate
layers of the algorithm were used to
predict behavior and neural activity
throughout a sensorimotor pathway.