Conference on Cognitive Computational Neuroscience

Deep Reinforcement learning is beginning to be useful for studying neural representations in the brain because of its ability to combine decision making and representation. Here, we use it to study a dot motion perceptual decision-making task in a high dimensional setting where the inputs are akin to those used in psychological experiments. This end-to-end model gives a unique insight into how these networks solve the task providing a background on how the brain could solve this task. We find that the network is able to show properties similar to the middle temporal visual area (MT) in the brain, which code for direction and motion strength. We find the emergence of direction selectivity purely through reward-based training and graded firing coding motion strength and make a testable prediction that the MT population would also have coherence selective neurons.