We use efficient coding principles borrowed from sensory neuroscience to derive the optimal
neural population to encode a reward distribution. We show that the responses of …

Population coding is widely regarded as an important mechanism for achieving reliable
behavioral responses despite neuronal variability. However, standard reinforcement …

Learning about rewards and punishments is critical for survival. Classical studies have
demonstrated an impressive correspondence between the firing of dopamine neurons in the …

Standard models of reinforcement learning in the brain assume that dopamine codes reward
prediction errors, and these reward prediction errors are integrated by the striatum to …

Since its introduction, the reward prediction error theory of dopamine has explained a wealth
of empirical phenomena, providing a unifying framework for understanding the …

Dopamine neurons facilitate learning by calculating reward prediction error, or the difference
between expected and actual reward. Despite two decades of research, it remains unclear …

Muller et al. demonstrate that reward signals recorded from the frontal cortex of nonhuman
primates exhibit a population-based scheme for learning probability distributions over …

Neural computational accounts of reward‐learning have been dominated by the hypothesis
that dopamine neurons behave like a reward‐prediction error and thus facilitate …

To efficiently represent all of the possible rewards in the world, dopaminergic midbrain
neurons dynamically adapt their coding range to the momentarily available rewards …

The ability to make appropriate choices is critical for survival. Successful decision making
requires the integration of sensory information, motivational states and potential outcomes to …