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A mathematical model describes the function of dopaminergic neurons in both learning and action planning.

A novel Bayesian method of modeling retinotopic maps is more accurate than traditional voxel-wise methods and can be used to automatically derive high-quality maps.

Evidence for multiple brain systems for sequence processing involving statistical inferences at multiple scales.

Emergent property inference, a novel machine learning methodology, learns distributions of neural circuit model parameters that produce computational properties and provides novel scientific insight through the quantification of the rich parametric structure it captures.

Computational modeling offers an explanation for why animals learn more quickly or slowly when their environment becomes more variable or stable.

A Bayesian model of phase angles illustrates a novel approach to the analysis of phase coherence in frequency-tagged experiments.

Midbrain dopamine neurons in rats signal discrepancies between predicted and actual rewards, regardless of whether the rewards are predicted on the basis of experience or inference.

Core associative learning phenomena that are observed in studies of eyeblink conditioning are also observed in sensorimotor adaptation, pointing to a common framework for these distinct cerebellar-dependent motor learning processes.

IL-27 exerts differential activation of STAT1 and STAT3 via IL-27Ra and GP130, respectively, leading to a kinetic decoupling of its gene expression program, which contributes to tune its immuno-modulatory activities.

Keeping flexible adaptable representations of speech categories at different time scales allows the brain to maintain stable perception in the face of varying speech sound characteristics.