Using computational theory to constrain statistical models of neural data

doi:10.1101/104737

Paper

Using computational theory to constrain statistical models of neural data

Published Jan 31, 2017 · Scott W. Linderman, S. Gershman

Current Opinion in Neurobiology

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27

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1

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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.

Literature Review

Study Snapshot

Key takeawayComputational theories can constrain statistical models of neural data, enabling data-driven and statistically rigorous revision of neural models in computational neuroscience.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Using computational theory to constrain statistical models of neural data

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