Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images
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Mendeley | Further Information

{"title"=>"Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images", "type"=>"journal", "authors"=>[{"first_name"=>"Umut", "last_name"=>"Güçlü"}, {"first_name"=>"Marcel A. J.", "last_name"=>"van Gerven"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84928704499", "issn"=>"1553-7358", "doi"=>"10.1371/journal.pcbi.1003724", "pmid"=>"25101625", "isbn"=>"1553-734x", "pui"=>"607916964"}, "id"=>"6220388b-6d4c-3c19-8090-aa91f84ca236", "abstract"=>"Encoding and decoding in functional magnetic resonance imaging has recently emerged as an area of research to noninvasively characterize the relationship between stimulus features and human brain activity. To overcome the challenge of formalizing what stimulus features should modulate single voxel responses, we introduce a general approach for making directly testable predictions of single voxel responses to statistically adapted representations of ecologically valid stimuli. These representations are learned from unlabeled data without supervision. Our approach is validated using a parsimonious computational model of (i) how early visual cortical representations are adapted to statistical regularities in natural images and (ii) how populations of these representations are pooled by single voxels. This computational model is used to predict single voxel responses to natural images and identify natural images from stimulus-evoked multiple voxel responses. We show that statistically adapted low-level sparse and invariant representations of natural images better span the space of early visual cortical representations and can be more effectively exploited in stimulus identification than hand-designed Gabor wavelets. Our results demonstrate the potential of our approach to better probe unknown cortical representations.", "link"=>"http://www.mendeley.com/research/unsupervised-feature-learning-improves-prediction-human-brain-activity-response-natural-images-9", "reader_count"=>120, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>10, "Researcher"=>23, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>32, "Student > Postgraduate"=>9, "Student > Master"=>22, "Other"=>1, "Student > Bachelor"=>14, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>10, "Researcher"=>23, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>32, "Student > Postgraduate"=>9, "Student > Master"=>22, "Other"=>1, "Student > Bachelor"=>14, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>8, "Unspecified"=>10, "Environmental Science"=>1, "Agricultural and Biological Sciences"=>20, "Medicine and Dentistry"=>5, "Neuroscience"=>21, "Arts and Humanities"=>1, "Physics and Astronomy"=>3, "Psychology"=>19, "Computer Science"=>30, "Linguistics"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>8}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Neuroscience"=>{"Neuroscience"=>21}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Psychology"=>{"Psychology"=>19}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>20}, "Computer Science"=>{"Computer Science"=>30}, "Linguistics"=>{"Linguistics"=>2}, "Unspecified"=>{"Unspecified"=>10}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Greece"=>1, "Netherlands"=>3, "United States"=>8, "Japan"=>3, "Poland"=>1, "Italy"=>1, "United Kingdom"=>2, "Switzerland"=>1, "Germany"=>3, "India"=>1}, "group_count"=>4}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1626786"], "description"=>"<p>The SC2 model was more invariant than the GWP2 model and its invariance increased from V1 to V3. (A) Mean prediction across the voxels that survived the threshold of 0.1 in the case of (b). Error bars show 1 SEM across the voxels (bootstrapping method). (B) Identification accuracy. Error bars show 1 SEM across the images in the validation set (bootstrapping method).</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Cognitive neuroscience", "sc2", "gwp2", "translating", "images", "validation"], "article_id"=>1131789, "categories"=>["Biological Sciences"], "users"=>["Umut Güçlü", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003724.g009", "stats"=>{"downloads"=>1, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_prediction_and_identification_accuracy_of_the_SC2_and_GWP2_models_after_a_and_before_b_translating_the_images_in_the_validation_set_by_0_8_in_a_random_dimension_/1131789", "title"=>"Mean prediction and identification accuracy of the SC2 and GWP2 models after (a) and before (b) translating the images in the validation set by 0.8° in a random dimension.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-07 03:26:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1626771"], "description"=>"<p>The phase, location, orientation and spatial frequency preference of the simple and complex cells were quantified as the corresponding parameters of Gabor wavelets that were fit to their receptive fields. Each pixel in a parameter map shows the corresponding preferred parameter of a simple or complex cell. The adjacent simple and complex cells had similar location, orientation and spatial frequency preference but different phase preference.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Cognitive neuroscience", "parameter", "maps", "sc"], "article_id"=>1131774, "categories"=>["Biological Sciences"], "users"=>["Umut Güçlü", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003724.g003", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Preferred_parameter_maps_of_the_SC_model_/1131774", "title"=>"Preferred parameter maps of the SC model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-07 03:26:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1626768"], "description"=>"<p>(A) Simple cell receptive fields of the SC model. Each square is of size 3232 pixels and shows the inverse weights between the input and a simple cell. The receptive fields were topographically organized, spatially localized, oriented and bandpass, similar to those found in the primary visual cortex. (B) Simple cell receptive fields of the GWP model. Each square is of size 128128 pixels and shows an even-symmetric Gabor wavelet. The grids show the locations of the remaining Gabor wavelets that were used. The receptive fields spanned eight orientations and six spatial frequencies.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Cognitive neuroscience", "receptive"], "article_id"=>1131771, "categories"=>["Biological Sciences"], "users"=>["Umut Güçlü", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003724.g002", "stats"=>{"downloads"=>4, "page_views"=>36, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simple_cell_receptive_fields_/1131771", "title"=>"Simple cell receptive fields.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-07 03:26:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1626785"], "description"=>"<p>The decoding performance was defined as the accuracy of identifying the 120 images in the validation set from a set of 9264 candidate images. The decoding performance of the SC2 model was significantly higher than that of the GWP2 model. Error bars show 1 SEM across the images in the validation set (bootstrapping method). A more detailed figure that shows the identified images is provided at <a href=\"http://www.ccnlab.net/research/\" target=\"_blank\">http://www.ccnlab.net/research/</a>.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Cognitive neuroscience", "sc2", "gwp2"], "article_id"=>1131788, "categories"=>["Biological Sciences"], "users"=>["Umut Güçlü", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003724.g008", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Decoding_performance_of_the_SC2_and_GWP2_models_/1131788", "title"=>"Decoding performance of the SC2 and GWP2 models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-07 03:26:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1626780"], "description"=>"<p>The eccentricity and size of the receptive fields were quantified as the mean and standard deviation of two-dimensional Gaussian functions that were fit to the voxel responses to point stimuli at different locations, respectively. The receptive field size systematically increased from low to high receptive field eccentricity and from area V1 to V3. Error bars show 1 SEM across the voxels (bootstrapping method).</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Cognitive neuroscience", "sc2", "receptive", "eccentricity"], "article_id"=>1131783, "categories"=>["Biological Sciences"], "users"=>["Umut Güçlü", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003724.g006", "stats"=>{"downloads"=>1, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Receptive_field_size_of_the_SC2_model_as_a_function_of_receptive_field_eccentricity_of_the_SC2_model_and_area_/1131783", "title"=>"Receptive field size of the SC2 model as a function of receptive field eccentricity of the SC2 model and area.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-07 03:26:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1626778"], "description"=>"<p>The parameter tuning varied across the voxels and had a bias for high spatial frequencies and oblique orientations. (A) Two-dimensional Gaussian functions that were fit to the responses of three representative voxels to point stimuli at different locations. (B) Responses of three representative voxels to sine-wave gratings that spanned a range of orientations and spatial frequencies. (C) Mean responses across the voxels to sine-wave gratings that spanned a range of orientations and spatial frequencies.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Cognitive neuroscience", "fields", "sc2"], "article_id"=>1131782, "categories"=>["Biological Sciences"], "users"=>["Umut Güçlü", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003724.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Receptive_fields_of_the_SC2_model_/1131782", "title"=>"Receptive fields of the SC2 model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-07 03:26:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1626787"], "description"=>"<p>The mean prediction of the linear one-layer models were below the threshold of 0.1. The mean prediction of the nonlinear SC models were significantly better than those of the nonlinear GWP models. The compressive nonlinearity and the nonlinear second layer increased the mean prediction of the linear and compressive nonlinear models, respectively. The nonlinear second layer increased the mean prediction of the compressive nonlinear SC model more than it increased that of the compressive nonlinear GWP model. The error bars show 1 SEM across the voxels (bootstrapping method).</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Cognitive neuroscience", "linear", "one-layer", "compressive", "nonlinear", "two-layer", "sc", "gwp", "voxels", "survived"], "article_id"=>1131790, "categories"=>["Biological Sciences"], "users"=>["Umut Güçlü", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003724.g010", "stats"=>{"downloads"=>2, "page_views"=>27, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_prediction_of_the_linear_one_layer_l_compressive_nonlinear_one_layer_cn_and_nonlinear_two_layer_2_SC_and_GWP_models_across_the_voxels_that_survived_the_threshold_of_0_1_in_the_case_of_2_/1131790", "title"=>"Mean prediction of the linear one-layer (l), compressive nonlinear one-layer (cn) and nonlinear two-layer (2) SC and GWP models across the voxels that survived the threshold of 0.1 in the case of (2).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-07 03:26:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1626783"], "description"=>"<p>The encoding performance was defined as between the observed and predicted voxel responses to the 120 images in the validation set across the two subjects. The encoding performance of the SC2 model was significantly higher than that of the GWP2 model. (A) Prediction across the voxels that survived the threshold of 0.1. (B) Mean prediction across the voxels that survived the threshold of 0.1. Error bars show 1 SEM across the voxels (bootstrapping method). (C) Prediction in each voxel.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Cognitive neuroscience", "sc2", "gwp2"], "article_id"=>1131787, "categories"=>["Biological Sciences"], "users"=>["Umut Güçlü", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003724.g007", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Encoding_performance_of_the_SC2_and_GWP2_models_/1131787", "title"=>"Encoding performance of the SC2 and GWP2 models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-07 03:26:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1626765"], "description"=>"<p>The encoding model predicts single voxel responses to images by nonlinearly transforming the images to complex cell responses and linearly transforming the complex cell responses to the single voxel responses. For example, the encoding model predicts a voxel response to a 128128 image as follows: Each of the 16 non-overlapping 3232 patches of the image is first vectorized, preprocessed and linearly transformed to 625 simple cell responses, i.e. where is a vectorized and preprocessed patch. Energies of the simple cells that are in each of the 625 partially overlapping 55 neighborhoods are then locally pooled, i.e. , and nonlinearly transformed to one complex cell response, i.e. . Next, 10000 complex cell responses are linearly transformed to the voxel response, i.e. where . The feature transformations are learned from unlabeled data. The voxel transformations are learned from feature-transformed stimulus-response pairs.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Cognitive neuroscience"], "article_id"=>1131768, "categories"=>["Biological Sciences"], "users"=>["Umut Güçlü", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003724.g001", "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Encoding_model_/1131768", "title"=>"Encoding model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-07 03:26:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/1626774"], "description"=>"<p>The population phase, location, orientation and spatial frequency tunings of the simple (solid lines) and complex cells (dashed lines) were quantified by fitting Gaussian functions to the median of their responses to Gabor wavelets that had different parameters. Each curve shows the median of their responses as a function of change in their preferred parameter. The complex cells were more invariant to phase and location than the simple cells.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Cognitive neuroscience", "parameter", "tuning", "curves", "sc"], "article_id"=>1131777, "categories"=>["Biological Sciences"], "users"=>["Umut Güçlü", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003724.g004", "stats"=>{"downloads"=>0, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Population_parameter_tuning_curves_of_the_SC_model_/1131777", "title"=>"Population parameter tuning curves of the SC model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-07 03:26:57"}

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