Inferring brain-computational mechanisms with models of activity measurements

TL;DR

This paper introduces a probabilistic RSA method with measurement models to infer brain-computational mechanisms from activity measurements, accounting for measurement effects and enabling Bayesian model comparison.

Contribution

It presents a novel approach combining measurement models with representational dissimilarity matrices for more accurate inference of brain models from activity data.

Findings

Measurement sampling significantly influences apparent dissimilarities.

Modeling measurement processes improves model distinguishability.

Bayesian inference effectively identifies the data-generating model.

Abstract

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer, which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in fMRI voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. In order to avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic RSA (pRSA) with measurement models, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognise the data-generating model in each case.