Abstract
Learning to select appropriate actions based on their values is a fundamentally human task which draws on the corticostriatal system in the brain. The prefrontal cortex (PFC) and dorsal striatum (dSTR) within this system are key in learning complex behaviors, and approaches from dynamical systems theory have allowed insight into how neural networks represent these behaviors. Yet, how learning itself is represented in top-down signals remains unknown. We hypothesized that learning is expressed in latent neural population dynamics. Therefore, we built a joint recurrent network model of the corticostriatal system and trained it on a complex learning task which involved executing the correct three-movement sequence. This system consisted of a striatal component which encoded action values and a prefrontal component which selected appropriate actions. After training, this system is able to autonomously predict value and select actions with the same performance as the animals trained on this task. We found that model representations mirrored those obtained from neural recordings in two macaques trained on the same task. We found that learning drove sequence-representations further apart from each other in latent space, both in our model and in the neural data. Our model revealed that learning proceeds by increasing the distance between sequence-specific fixed point regions and, hence, makes it more likely to select the appropriate action sequence. We also found that PFC sequence representations were more structured than dSTR representations. Altogether, we used a joint recurrent network model of the corticostriatal system together with neural recordings from the same regions to uncover the first evidence of how learning is expressed in top-down neural population signals within the corticostriatal system in the brain.