TY - JOUR T1 - Using computational theory to constrain statistical models of neural data JF - bioRxiv DO - 10.1101/104737 SP - 104737 AU - Scott W. Linderman AU - Samuel J. Gershman Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/01/31/104737.abstract N2 - Computational neuroscience is, to first order, dominated by two approaches: the “bottom-up” approach, which searches for statistical patterns in large-scale neural recordings, and the “top-down” approach, which begins with a theory of computation and considers plausible neural implementations. While this division is not clear-cut, we argue that these approaches should be much more intimately linked. From a Bayesian perspective, computational theories provide constrained prior distributions on neural data—albeit highly sophisticated ones. By connecting theory to observation via a probabilistic model, we provide the link necessary to test, evaluate, and revise our theories in a data-driven and statistically rigorous fashion. This review highlights examples of this theory-driven pipeline for neural data analysis in recent literature and illustrates it with a worked example based on the temporal difference learning model of dopamine. ER -