Using human brain activity to guide machine learning

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...

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Published in:	Scientific reports Vol. 8; no. 1; pp. 5397 - 10
Main Authors:	Fong, Ruth C., Scheirer, Walter J., Cox, David D.
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 29.03.2018 Nature Publishing Group Nature Portfolio
Subjects:	59/36 631/378/116/2396 639/705/117 Algorithms Artificial intelligence Brain Brain - diagnostic imaging Brain - physiology Brain mapping Functional magnetic resonance imaging Humanities and Social Sciences Humans Image Processing, Computer-Assisted Learning algorithms Machine Learning Magnetic Resonance Imaging multidisciplinary Neural networks Pattern recognition Science Science (multidisciplinary)
ISSN:	2045-2322, 2045-2322
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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.
AbstractList	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. 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. 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.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. 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.
ArticleNumber	5397
Author	Fong, Ruth C. Cox, David D. Scheirer, Walter J.
Author_xml	– sequence: 1 givenname: Ruth C. surname: Fong fullname: Fong, Ruth C. organization: Department of Engineering Science, University of Oxford, Information Engineering Building, Department of Molecular and Cellular Biology, School of Engineering and Applied Sciences and Center for Brain Science, Harvard University – sequence: 2 givenname: Walter J. surname: Scheirer fullname: Scheirer, Walter J. organization: Department of Computer Science and Engineering, University of Notre Dame, Fitzpatrick Hall of Engineering, Department of Molecular and Cellular Biology, School of Engineering and Applied Sciences and Center for Brain Science, Harvard University – sequence: 3 givenname: David D. surname: Cox fullname: Cox, David D. email: davidcox@fas.harvard.edu organization: Department of Molecular and Cellular Biology, School of Engineering and Applied Sciences and Center for Brain Science, Harvard University
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Cites_doi	10.1101/pdb.err084830 10.1109/TNN.2010.2060353 10.1109/TIP.2012.2210727 10.1152/jn.1987.57.3.835 10.1016/S1053-8119(03)00049-1 10.1038/nature14236 10.1016/j.tics.2009.06.002 10.1002/hbm.20169 10.1126/science.1152876 10.1016/S0042-6989(97)00464-1 10.1109/TPAMI.2013.2297711 10.1016/j.tics.2006.05.009 10.1016/j.neuron.2009.09.006 10.1016/j.neuroimage.2009.11.084 10.1073/pnas.0700622104 10.1073/pnas.1403112111 10.1016/j.neuron.2013.06.034 10.1098/rspb.1980.0020 10.1109/TPAMI.2009.167 10.1038/nature20101 10.1038/nature06713 10.1038/14819 10.1038/340386a0 10.1097/00001756-199901180-00035 10.1002/hbm.20379 10.1073/pnas.0705654104 10.1109/TPAMI.2015.2439285 10.1145/1553374.1553380 10.1109/CVPR.2017.479 10.1371/journal.pcbi.1000579 10.1109/CVPR.2015.7298932 10.1109/CVPR.2016.177 10.1017/S0140525X16001837 10.1109/CVPR.2009.5206651 10.7551/mitpress/1130.003.0016 10.1109/CVPR.2014.222 10.1038/srep27755 10.1109/CVPR.2015.7298799 10.1109/CVPR.2014.223 10.1145/1961189.1961199 10.1109/CVPR.2015.7298640 10.1109/CVPR.2014.22 10.3389/neuro.06.004.2008 10.1007/978-3-540-74198-5_14 10.1007/978-3-642-33712-3_25 10.1109/ICCV.2007.4408844 10.1613/jair.295 10.21236/ADA612443 10.7551/mitpress/1113.003.0014
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References	Mnih (CR31) 2015; 518 CR39 Borji, Sihite, Itti (CR4) 2013; 22 CR38 CR37 CR36 CR34 Spiridon, Fischl, Kanwisher. (CR52) 2006; 27 CR32 Lueck (CR44) 1989; 340 Ohki, Reid (CR60) 2014; 2014 Stansbury, Naselaris, Gallant (CR41) 2013; 79 Deng, Krause, Stark, Fei-Fei (CR59) 2016; 38 Chen, Li, Kourtzi, Wu (CR35) 2010; 21 Tenenbaum, Griffiths, Kemp (CR16) 2006; 10 CR2 CR3 CR6 CR5 CR7 Scheirer (CR8) 2014; 36 CR9 CR49 CR48 CR40 Mitchell (CR19) 2008; 320 Mnih (CR15) 2015; 518 Kriegeskorte (CR51) 2007; 104 Reddy, Tsuchiya, Serre (CR21) 2010; 50 Serre, Oliva, Poggio (CR28) 2007; 104 Russakovsky (CR1) 2014; 115 Jäkel (CR17) 2009; 13 CR14 Xu (CR33) 2015; 2 CR58 CR13 CR12 Riesenhuber, Poggio (CR27) 1999; 2 CR56 CR11 CR55 CR10 CR54 CR53 Graves (CR30) 2016; 538 Lee, Mumford, Romero, Lamme (CR42) 1998; 38 Desimone, Schein (CR45) 1987; 57 Kay, David, Prenger, Hansen, Gallant (CR64) 2008; 29 Kanwisher, Stanley, Harris (CR47) 1999; 10 Kay (CR18) 2008; 452 Naselaris (CR20) 2009; 63 Felzenszwalb (CR66) 2010; 32 Settles (CR57) 2010; 52 CR29 Cox, Savoy (CR50) 2003; 19 CR26 CR25 CR69 CR24 Cortes, Vapnik (CR46) 1995; 20 CR68 CR67 CR22 Yamins (CR23) 2014; 111 CR65 CR63 CR62 CR61 Marr, Hildreth (CR43) 1980; 207 T Serre (23618_CR28) 2007; 104 23618_CR36 A Borji (23618_CR4) 2013; 22 23618_CR37 23618_CR38 23618_CR32 23618_CR34 O Russakovsky (23618_CR1) 2014; 115 23618_CR39 L Reddy (23618_CR21) 2010; 50 D Chen (23618_CR35) 2010; 21 N Kanwisher (23618_CR47) 1999; 10 T Naselaris (23618_CR20) 2009; 63 K Xu (23618_CR33) 2015; 2 KN Kay (23618_CR64) 2008; 29 JB Tenenbaum (23618_CR16) 2006; 10 F Jäkel (23618_CR17) 2009; 13 23618_CR24 23618_CR68 23618_CR25 B Settles (23618_CR57) 2010; 52 23618_CR69 23618_CR26 D Yamins (23618_CR23) 2014; 111 D Stansbury (23618_CR41) 2013; 79 23618_CR65 23618_CR22 23618_CR67 23618_CR29 V Mnih (23618_CR31) 2015; 518 TM Mitchell (23618_CR19) 2008; 320 PF Felzenszwalb (23618_CR66) 2010; 32 C Lueck (23618_CR44) 1989; 340 M Riesenhuber (23618_CR27) 1999; 2 R Desimone (23618_CR45) 1987; 57 23618_CR61 23618_CR62 23618_CR63 23618_CR13 23618_CR14 23618_CR58 C Cortes (23618_CR46) 1995; 20 23618_CR53 23618_CR10 N Kriegeskorte (23618_CR51) 2007; 104 23618_CR54 23618_CR11 23618_CR55 23618_CR12 23618_CR56 23618_CR2 D Marr (23618_CR43) 1980; 207 23618_CR3 23618_CR5 23618_CR6 23618_CR7 J Deng (23618_CR59) 2016; 38 23618_CR9 DD Cox (23618_CR50) 2003; 19 TS Lee (23618_CR42) 1998; 38 23618_CR48 23618_CR49 V Mnih (23618_CR15) 2015; 518 K Kay (23618_CR18) 2008; 452 A Graves (23618_CR30) 2016; 538 WJ Scheirer (23618_CR8) 2014; 36 K Ohki (23618_CR60) 2014; 2014 23618_CR40 M Spiridon (23618_CR52) 2006; 27
References_xml	– ident: CR22 – ident: CR49 – volume: 2014 start-page: pdb year: 2014 end-page: prot081455 ident: CR60 article-title: two-photon calcium imaging in the visual system publication-title: Cold Spring Harbor Protocols doi: 10.1101/pdb.err084830 – ident: CR68 – volume: 21 start-page: 1680 year: 2010 end-page: 1685 ident: CR35 article-title: Behavior-constrained support vector machines for fMRI data analysis publication-title: IEEE T-NN doi: 10.1109/TNN.2010.2060353 – volume: 22 start-page: 55 year: 2013 end-page: 69 ident: CR4 article-title: Quantitative analysis of human-model agreement in visual saliency modeling: A comparative study publication-title: IEEE T-IP doi: 10.1109/TIP.2012.2210727 – ident: CR39 – ident: CR12 – ident: CR29 – volume: 57 start-page: 835 year: 1987 end-page: 868 ident: CR45 article-title: Visual properties of neurons in area v4 of the macaque: sensitivity to stimulus form publication-title: Journal of neurophysiology doi: 10.1152/jn.1987.57.3.835 – ident: CR54 – ident: CR61 – volume: 19 start-page: 261 year: 2003 end-page: 270 ident: CR50 article-title: Functional magnetic resonance imaging (fMRI) brain reading: detecting and classifying distributed patterns of fMRI activity in human visual cortex publication-title: Neuroimage doi: 10.1016/S1053-8119(03)00049-1 – ident: CR58 – volume: 518 start-page: 529 year: 2015 end-page: 533 ident: CR15 article-title: Human-level control through deep reinforcement learning publication-title: Nature doi: 10.1038/nature14236 – volume: 13 start-page: 381 year: 2009 end-page: 388 ident: CR17 article-title: Does cognitive science need kernels? publication-title: Trends in Cog. Sci. doi: 10.1016/j.tics.2009.06.002 – ident: CR25 – volume: 27 start-page: 77 year: 2006 end-page: 89 ident: CR52 article-title: Location and spatial profile of category-specific regions in human extrastriate cortex publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.20169 – volume: 20 start-page: 273 year: 1995 end-page: 297 ident: CR46 article-title: Support-vector networks publication-title: Machine learning – volume: 320 start-page: 1191 year: 2008 end-page: 1195 ident: CR19 article-title: Predicting human brain activity associated with the meanings of nouns publication-title: Science doi: 10.1126/science.1152876 – volume: 2 start-page: 5 year: 2015 ident: CR33 article-title: Show, attend and tell: Neural image caption generation with visual attention publication-title: arXiv preprint arXiv:1502.03044 – ident: CR67 – volume: 38 start-page: 2429 year: 1998 end-page: 2454 ident: CR42 article-title: The role of the primary visual cortex in higher level vision publication-title: Vision research doi: 10.1016/S0042-6989(97)00464-1 – ident: CR11 – volume: 36 start-page: 1679 year: 2014 end-page: 1686 ident: CR8 article-title: Perceptual annotation: Measuring human vision to improve computer vision publication-title: IEEE T-PAMI doi: 10.1109/TPAMI.2013.2297711 – ident: CR9 – ident: CR32 – volume: 10 start-page: 309 year: 2006 end-page: 318 ident: CR16 article-title: Theory-based bayesian models of inductive learning and reasoning publication-title: Trends in Cog. Sci. doi: 10.1016/j.tics.2006.05.009 – volume: 63 start-page: 902 year: 2009 end-page: 915 ident: CR20 article-title: Bayesian reconstruction of natural images from human brain activity publication-title: Neuron doi: 10.1016/j.neuron.2009.09.006 – ident: CR36 – ident: CR5 – volume: 50 start-page: 818 year: 2010 end-page: 825 ident: CR21 article-title: Reading the mind’s eye: decoding category information during mental imagery publication-title: Neuroimage doi: 10.1016/j.neuroimage.2009.11.084 – volume: 104 start-page: 6424 year: 2007 end-page: 6429 ident: CR28 article-title: A feedforward architecture accounts for rapid categorization publication-title: PNAS doi: 10.1073/pnas.0700622104 – volume: 111 start-page: 8619 year: 2014 end-page: 8624 ident: CR23 article-title: Performance-optimized hierarchical models predict neural responses in higher visual cortex publication-title: PNAS doi: 10.1073/pnas.1403112111 – ident: CR26 – volume: 79 start-page: 1025 year: 2013 end-page: 1034 ident: CR41 article-title: Natural scene statistics account for the representation of scene categories in human visual cortex publication-title: Neuron doi: 10.1016/j.neuron.2013.06.034 – volume: 207 start-page: 187 year: 1980 end-page: 217 ident: CR43 article-title: Theory of edge detection publication-title: Proceedings of the Royal Society of London B: Biological Sciences doi: 10.1098/rspb.1980.0020 – volume: 32 start-page: 1627 year: 2010 end-page: 1645 ident: CR66 article-title: Object detection with discriminatively trained part-based models publication-title: IEEE T-PAMI doi: 10.1109/TPAMI.2009.167 – volume: 115 start-page: 1 year: 2014 end-page: 42 ident: CR1 article-title: Imagenet large scale visual recognition challenge publication-title: IJCV – ident: CR14 – ident: CR2 – ident: CR37 – ident: CR53 – volume: 538 start-page: 471 year: 2016 end-page: 476 ident: CR30 article-title: Hybrid computing using a neural network with dynamic external memory publication-title: Nature doi: 10.1038/nature20101 – ident: CR10 – volume: 452 start-page: 352 year: 2008 end-page: 355 ident: CR18 article-title: Identifying natural images from human brain activity publication-title: Nature doi: 10.1038/nature06713 – ident: CR6 – volume: 518 start-page: 529 year: 2015 end-page: 533 ident: CR31 article-title: Human-level control through deep reinforcement learning publication-title: Nature doi: 10.1038/nature14236 – volume: 2 start-page: 1019 year: 1999 end-page: 1025 ident: CR27 article-title: Hierarchical models of object recognition in cortex publication-title: Nature Neuroscience doi: 10.1038/14819 – ident: CR56 – volume: 340 start-page: 386 year: 1989 end-page: 389 ident: CR44 article-title: The colour centre in the cerebral cortex of man publication-title: Nature doi: 10.1038/340386a0 – ident: CR40 – ident: CR63 – volume: 10 start-page: 183 year: 1999 end-page: 187 ident: CR47 article-title: The fusiform face area is selective for faces not animals publication-title: Neuroreport doi: 10.1097/00001756-199901180-00035 – ident: CR69 – ident: CR48 – ident: CR65 – ident: CR3 – ident: CR38 – volume: 29 start-page: 142 year: 2008 end-page: 156 ident: CR64 article-title: Modeling low-frequency fluctuation and hemodynamic response timecourse in event-related fmri publication-title: Human brain mapping doi: 10.1002/hbm.20379 – volume: 52 start-page: 11 year: 2010 ident: CR57 article-title: Active learning literature survey publication-title: University of Wisconsin, Madison – ident: CR13 – volume: 104 start-page: 20600 year: 2007 end-page: 20605 ident: CR51 article-title: Individual faces elicit distinct response patterns in human anterior temporal cortex publication-title: PNAS doi: 10.1073/pnas.0705654104 – ident: CR34 – ident: CR55 – ident: CR7 – ident: CR62 – ident: CR24 – volume: 38 start-page: 666 year: 2016 end-page: 676 ident: CR59 article-title: Leveraging the wisdom of the crowd for fine-grained recognition publication-title: IEEE T-PAMI doi: 10.1109/TPAMI.2015.2439285 – ident: 23618_CR37 doi: 10.1145/1553374.1553380 – volume: 52 start-page: 11 year: 2010 ident: 23618_CR57 publication-title: University of Wisconsin, Madison – volume: 21 start-page: 1680 year: 2010 ident: 23618_CR35 publication-title: IEEE T-NN doi: 10.1109/TNN.2010.2060353 – ident: 23618_CR36 doi: 10.1109/CVPR.2017.479 – ident: 23618_CR29 doi: 10.1371/journal.pcbi.1000579 – ident: 23618_CR3 doi: 10.1109/CVPR.2015.7298932 – volume: 518 start-page: 529 year: 2015 ident: 23618_CR15 publication-title: Nature doi: 10.1038/nature14236 – volume: 340 start-page: 386 year: 1989 ident: 23618_CR44 publication-title: Nature doi: 10.1038/340386a0 – ident: 23618_CR69 – volume: 36 start-page: 1679 year: 2014 ident: 23618_CR8 publication-title: IEEE T-PAMI doi: 10.1109/TPAMI.2013.2297711 – volume: 2 start-page: 5 year: 2015 ident: 23618_CR33 publication-title: arXiv preprint arXiv:1502.03044 – ident: 23618_CR9 doi: 10.1109/CVPR.2016.177 – volume: 10 start-page: 309 year: 2006 ident: 23618_CR16 publication-title: Trends in Cog. Sci. doi: 10.1016/j.tics.2006.05.009 – ident: 23618_CR32 – ident: 23618_CR13 – volume: 27 start-page: 77 year: 2006 ident: 23618_CR52 publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.20169 – ident: 23618_CR49 – volume: 104 start-page: 20600 year: 2007 ident: 23618_CR51 publication-title: PNAS doi: 10.1073/pnas.0705654104 – volume: 38 start-page: 666 year: 2016 ident: 23618_CR59 publication-title: IEEE T-PAMI doi: 10.1109/TPAMI.2015.2439285 – ident: 23618_CR34 doi: 10.1017/S0140525X16001837 – ident: 23618_CR56 doi: 10.1109/CVPR.2009.5206651 – ident: 23618_CR68 doi: 10.7551/mitpress/1130.003.0016 – volume: 79 start-page: 1025 year: 2013 ident: 23618_CR41 publication-title: Neuron doi: 10.1016/j.neuron.2013.06.034 – volume: 518 start-page: 529 year: 2015 ident: 23618_CR31 publication-title: Nature doi: 10.1038/nature14236 – ident: 23618_CR39 doi: 10.1109/CVPR.2014.222 – ident: 23618_CR62 – volume: 20 start-page: 273 year: 1995 ident: 23618_CR46 publication-title: Machine learning – volume: 63 start-page: 902 year: 2009 ident: 23618_CR20 publication-title: Neuron doi: 10.1016/j.neuron.2009.09.006 – ident: 23618_CR26 doi: 10.1038/srep27755 – ident: 23618_CR10 – ident: 23618_CR6 doi: 10.1109/CVPR.2015.7298799 – volume: 32 start-page: 1627 year: 2010 ident: 23618_CR66 publication-title: IEEE T-PAMI doi: 10.1109/TPAMI.2009.167 – ident: 23618_CR2 doi: 10.1109/CVPR.2014.223 – ident: 23618_CR14 – volume: 115 start-page: 1 year: 2014 ident: 23618_CR1 publication-title: IJCV – ident: 23618_CR65 doi: 10.1145/1961189.1961199 – volume: 22 start-page: 55 year: 2013 ident: 23618_CR4 publication-title: IEEE T-IP doi: 10.1109/TIP.2012.2210727 – ident: 23618_CR12 doi: 10.1109/CVPR.2015.7298640 – ident: 23618_CR48 – volume: 207 start-page: 187 year: 1980 ident: 23618_CR43 publication-title: Proceedings of the Royal Society of London B: Biological Sciences doi: 10.1098/rspb.1980.0020 – volume: 452 start-page: 352 year: 2008 ident: 23618_CR18 publication-title: Nature doi: 10.1038/nature06713 – ident: 23618_CR5 doi: 10.1109/CVPR.2014.22 – volume: 104 start-page: 6424 year: 2007 ident: 23618_CR28 publication-title: PNAS doi: 10.1073/pnas.0700622104 – volume: 38 start-page: 2429 year: 1998 ident: 23618_CR42 publication-title: Vision research doi: 10.1016/S0042-6989(97)00464-1 – ident: 23618_CR24 doi: 10.3389/neuro.06.004.2008 – ident: 23618_CR25 – ident: 23618_CR40 – ident: 23618_CR38 – ident: 23618_CR63 – volume: 13 start-page: 381 year: 2009 ident: 23618_CR17 publication-title: Trends in Cog. Sci. doi: 10.1016/j.tics.2009.06.002 – ident: 23618_CR61 doi: 10.1007/978-3-540-74198-5_14 – volume: 57 start-page: 835 year: 1987 ident: 23618_CR45 publication-title: Journal of neurophysiology doi: 10.1152/jn.1987.57.3.835 – ident: 23618_CR7 doi: 10.1007/978-3-642-33712-3_25 – ident: 23618_CR11 – volume: 111 start-page: 8619 year: 2014 ident: 23618_CR23 publication-title: PNAS doi: 10.1073/pnas.1403112111 – volume: 2 start-page: 1019 year: 1999 ident: 23618_CR27 publication-title: Nature Neuroscience doi: 10.1038/14819 – volume: 10 start-page: 183 year: 1999 ident: 23618_CR47 publication-title: Neuroreport doi: 10.1097/00001756-199901180-00035 – volume: 2014 start-page: pdb year: 2014 ident: 23618_CR60 publication-title: Cold Spring Harbor Protocols doi: 10.1101/pdb.err084830 – volume: 538 start-page: 471 year: 2016 ident: 23618_CR30 publication-title: Nature doi: 10.1038/nature20101 – ident: 23618_CR55 doi: 10.1109/ICCV.2007.4408844 – volume: 50 start-page: 818 year: 2010 ident: 23618_CR21 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2009.11.084 – ident: 23618_CR22 – ident: 23618_CR54 doi: 10.1613/jair.295 – volume: 19 start-page: 261 year: 2003 ident: 23618_CR50 publication-title: Neuroimage doi: 10.1016/S1053-8119(03)00049-1 – ident: 23618_CR53 doi: 10.21236/ADA612443 – volume: 29 start-page: 142 year: 2008 ident: 23618_CR64 publication-title: Human brain mapping doi: 10.1002/hbm.20379 – ident: 23618_CR58 – volume: 320 start-page: 1191 year: 2008 ident: 23618_CR19 publication-title: Science doi: 10.1126/science.1152876 – ident: 23618_CR67 doi: 10.7551/mitpress/1113.003.0014
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Snippet	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... 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...
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SubjectTerms	59/36 631/378/116/2396 639/705/117 Algorithms Artificial intelligence Brain Brain - diagnostic imaging Brain - physiology Brain mapping Functional magnetic resonance imaging Humanities and Social Sciences Humans Image Processing, Computer-Assisted Learning algorithms Machine Learning Magnetic Resonance Imaging multidisciplinary Neural networks Pattern recognition Science Science (multidisciplinary)
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Title	Using human brain activity to guide machine learning
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