RT Journal Article
SR Electronic
T1 Using computational theory to constrain statistical models of neural data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 104737
DO 10.1101/104737
A1 Scott W. Linderman
A1 Samuel J. Gershman
YR 2017
UL http://biorxiv.org/content/early/2017/01/31/104737.abstract
AB 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.