Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments.

ABSTRACT: Humans possess an exceptional aptitude to efficiently make decisions from high-dimensional sensory observations. However, it is unknown how the brain compactly represents the current state of the environment to guide this process. The deep Q-network (DQN) achieves this by capturing highly nonlinear mappings from multivariate inputs to the values of potential actions. We deployed DQN as a model of brain activity and behavior in participants playing three Atari video games during fMRI. Hidden layers of DQN exhibited a striking resemblance to voxel activity in a distributed sensorimotor network, extending throughout the dorsal visual pathway into posterior parietal cortex. Neural state-space representations emerged from nonlinear transformations of the pixel space bridging perception to actio

SUBMITTER: Cross L 

PROVIDER: S-EPMC7897245 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature