Using computational theory to constrain statistical models of neural data

作者：Linderman Scott W; Gershman Samuel J^*

来源：Current Opinion in Neurobiology, 2017, 46: 14-24.

DOI：10.1016/j.conb.2017.06.004

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.

• 出版日期2017-10