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bioRxiv · 10.1101/104737

Using computational theory to constrain statistical models of neural data

Linderman, S. W.·Gershman, S. J.
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Abstract

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.

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Linderman, S. W., Gershman, S. J.. 2017-01-31. Using computational theory to constrain statistical models of neural data. https://doi.org/10.1101/104737

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