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Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns

Jörn Diedrichsen, Atsushi Yokoi, Spencer A. Arbuckle
doi: https://doi.org/10.1101/120584
Jörn Diedrichsen
aBrain and Mind Institute, Western University, Canada
bDepartment of Statistical and Actuarial Sciences, Western University, Canada
cDepartment of Computer Science, Western University, Canada
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  • For correspondence: jdiedric@uwo.ca
Atsushi Yokoi
aBrain and Mind Institute, Western University, Canada
dGraduate School of Frontier Biosciences, Osaka University, Japan
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Spencer A. Arbuckle
aBrain and Mind Institute, Western University, Canada
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Abstract

Representational models specify how complex patterns of neural activity relate to visual stimuli, motor actions, or abstract thoughts. Here we review pattern component modeling (PCM), a practical Bayesian approach for evaluating such models. Similar to encoding models, PCM evaluates the ability of models to predict novel brain activity patterns. In contrast to encoding models, however, the activity of individual voxels across conditions (activity profiles) are not directly fitted. Rather, PCM integrates over all possible activity profiles and computes the marginal likelihood of the data under the activity profile distribution specified by the representational model. By using an analytical expression for the marginal likelihood, PCM allows the fitting of flexible representational models, in which the relative strength and form of the encoded features can be estimated from the data. We discuss here a number of different forms with which such flexible representational models can be specified, and how models of different complexity can be compared. We then provide a number of practical examples from our recent work in motor control, ranging from fixed models to more complex non-linear models of brain representations. The code for the fitting and cross-validation of representational models is provided in a open-source Matlab toolbox.

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Posted March 25, 2017.
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Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns
Jörn Diedrichsen, Atsushi Yokoi, Spencer A. Arbuckle
bioRxiv 120584; doi: https://doi.org/10.1101/120584
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Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns
Jörn Diedrichsen, Atsushi Yokoi, Spencer A. Arbuckle
bioRxiv 120584; doi: https://doi.org/10.1101/120584

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