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Inferring brain-computational mechanisms with models of activity measurements

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) t...

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Detalles Bibliográficos
Autores principales: Kriegeskorte, Nikolaus, Diedrichsen, Jörn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5003864/
https://www.ncbi.nlm.nih.gov/pubmed/27574316
http://dx.doi.org/10.1098/rstb.2016.0278
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author Kriegeskorte, Nikolaus
Diedrichsen, Jörn
author_facet Kriegeskorte, Nikolaus
Diedrichsen, Jörn
author_sort Kriegeskorte, Nikolaus
collection PubMed
description 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 functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. 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 representational similarity analysis (pRSA) with MMs, 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 recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.
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spelling pubmed-50038642016-10-05 Inferring brain-computational mechanisms with models of activity measurements Kriegeskorte, Nikolaus Diedrichsen, Jörn Philos Trans R Soc Lond B Biol Sci Articles 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 functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. 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 representational similarity analysis (pRSA) with MMs, 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 recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’. The Royal Society 2016-10-05 /pmc/articles/PMC5003864/ /pubmed/27574316 http://dx.doi.org/10.1098/rstb.2016.0278 Text en © 2016 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Kriegeskorte, Nikolaus
Diedrichsen, Jörn
Inferring brain-computational mechanisms with models of activity measurements
title Inferring brain-computational mechanisms with models of activity measurements
title_full Inferring brain-computational mechanisms with models of activity measurements
title_fullStr Inferring brain-computational mechanisms with models of activity measurements
title_full_unstemmed Inferring brain-computational mechanisms with models of activity measurements
title_short Inferring brain-computational mechanisms with models of activity measurements
title_sort inferring brain-computational mechanisms with models of activity measurements
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5003864/
https://www.ncbi.nlm.nih.gov/pubmed/27574316
http://dx.doi.org/10.1098/rstb.2016.0278
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