Using human brain activity to guide machine learning

Abstract Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspira...

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Autores principales:	Ruth C. Fong, Walter J. Scheirer, David D. Cox
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2e55a068e7f449f5ad9e29c3ff4f18bd
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id	oai:doaj.org-article:2e55a068e7f449f5ad9e29c3ff4f18bd
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spelling	oai:doaj.org-article:2e55a068e7f449f5ad9e29c3ff4f18bd2021-12-02T15:07:52ZUsing human brain activity to guide machine learning10.1038/s41598-018-23618-62045-2322https://doaj.org/article/2e55a068e7f449f5ad9e29c3ff4f18bd2018-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-23618-6https://doaj.org/toc/2045-2322Abstract Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of “neurally-weighted” machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.Ruth C. FongWalter J. ScheirerDavid D. CoxNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ruth C. Fong Walter J. Scheirer David D. Cox Using human brain activity to guide machine learning
description	Abstract Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of “neurally-weighted” machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.
format	article
author	Ruth C. Fong Walter J. Scheirer David D. Cox
author_facet	Ruth C. Fong Walter J. Scheirer David D. Cox
author_sort	Ruth C. Fong
title	Using human brain activity to guide machine learning
title_short	Using human brain activity to guide machine learning
title_full	Using human brain activity to guide machine learning
title_fullStr	Using human brain activity to guide machine learning
title_full_unstemmed	Using human brain activity to guide machine learning
title_sort	using human brain activity to guide machine learning
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/2e55a068e7f449f5ad9e29c3ff4f18bd
work_keys_str_mv	AT ruthcfong usinghumanbrainactivitytoguidemachinelearning AT walterjscheirer usinghumanbrainactivitytoguidemachinelearning AT daviddcox usinghumanbrainactivitytoguidemachinelearning
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Using human brain activity to guide machine learning

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