Using computational theory to constrain statistical models of neural data

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.

Functional Connectivity Basis and Underlying Cognitive Mechanisms for Gender Differences in Guilt Aversion

“…For the model validation and selection, 10 possible models were compared based on the predictive negative log likelihoods by a cross-validation. This cross-validation approach for value-based decision-making allows us to avoid overfitting the data and to compare models with different numbers of parameters robustly; it has also been adopted in many recent studies ( Daw, 2011 ; Smith et al, 2014 ; Linderman and Gershman, 2017 ; Park et al, 2019 ; Fig. 2 B ; see also Model validation and comparison in Materials and Methods).…”

“…This cross-validation approach for value-based decision-making allows us to avoid overfitting the data and to compare models with different numbers of parameters robustly. It has also been adopted in many recent studies ( Daw, 2011 ; Smith et al, 2014 ; Linderman and Gershman, 2017 ; Park et al, 2019 ; Fig. 2 B ).…”