A Computational Model of the Development of Separate Representations of Facial Identity and Expression in the Primate Visual System
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{"title"=>"A computational model of the development of separate representations of facial identity and expression in the primate visual system", "type"=>"journal", "authors"=>[{"first_name"=>"James Matthew", "last_name"=>"Tromans", "scopus_author_id"=>"22952398400"}, {"first_name"=>"Mitchell", "last_name"=>"Harris", "scopus_author_id"=>"53881199600"}, {"first_name"=>"Simon Maitland", "last_name"=>"Stringer", "scopus_author_id"=>"7006217098"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"362707314", "sgr"=>"80053613744", "issn"=>"19326203", "pmid"=>"21998673", "scopus"=>"2-s2.0-80053613744", "doi"=>"10.1371/journal.pone.0025616", "isbn"=>"1932-6203 (Electronic)\r1932-6203 (Linking)"}, "id"=>"c09d72f7-719b-30d3-9c8f-dd8f7b641991", "abstract"=>"Experimental studies have provided evidence that the visual processing areas of the primate brain represent facial identity and facial expression within different subpopulations of neurons. For example, in non-human primates there is evidence that cells within the inferior temporal gyrus (TE) respond primarily to facial identity, while cells within the superior temporal sulcus (STS) respond to facial expression. More recently, it has been found that the orbitofrontal cortex (OFC) of non-human primates contains some cells that respond exclusively to changes in facial identity, while other cells respond exclusively to facial expression. How might the primate visual system develop physically separate representations of facial identity and expression given that the visual system is always exposed to simultaneous combinations of facial identity and expression during learning? In this paper, a biologically plausible neural network model, VisNet, of the ventral visual pathway is trained on a set of carefully-designed cartoon faces with different identities and expressions. The VisNet model architecture is composed of a hierarchical series of four Self-Organising Maps (SOMs), with associative learning in the feedforward synaptic connections between successive layers. During learning, the network develops separate clusters of cells that respond exclusively to either facial identity or facial expression. We interpret the performance of the network in terms of the learning properties of SOMs, which are able to exploit the statistical indendependence between facial identity and expression.", "link"=>"http://www.mendeley.com/research/computational-model-development-separate-representations-facial-identity-expression-primate-visual-s", "reader_count"=>31, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>8, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>7, "Student > Master"=>3, "Other"=>1, "Student > Bachelor"=>5, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>8, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>7, "Student > Master"=>3, "Other"=>1, "Student > Bachelor"=>5, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>1, "Agricultural and Biological Sciences"=>5, "Medicine and Dentistry"=>2, "Neuroscience"=>4, "Psychology"=>13, "Computer Science"=>3, "Decision Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>4}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Psychology"=>{"Psychology"=>13}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>5}, "Computer Science"=>{"Computer Science"=>3}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Iran"=>1, "Germany"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/727908"], "description"=>"<p>The sigmoid parameters used to control the global inhibition within each layer of the model.</p>", "links"=>[], "tags"=>["Mental health", "Computational biology", "neuroscience"], "article_id"=>398267, "categories"=>["Biological Sciences", "Neuroscience", "Mental Health"], "users"=>["James Matthew Tromans", "Mitchell Harris", "Simon Maitland Stringer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025616.t004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sigmoid_parameters_/398267", "title"=>"Sigmoid parameters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 14:05:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/727540"], "description"=>"<p>Analysis of information about expression conveyed by fourth (output) layer cells before and after training on 1600 complete faces. The information analysis was performed as described in the <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0025616#s4\" target=\"_blank\">Methods</a>. The plot shows the amount of single cell information carried by individual output cells in rank order. In the trained condition, 17 neurons provided maximal information of 2.32 bits. In the untrained condition, no cells reached maximal information.</p>", "links"=>[], "tags"=>["Mental health", "Computational biology", "neuroscience"], "article_id"=>397900, "categories"=>["Biological Sciences", "Neuroscience", "Mental Health"], "users"=>["James Matthew Tromans", "Mitchell Harris", "Simon Maitland Stringer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025616.g004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Information_analysis_for_expression_/397900", "title"=>"Information analysis for expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:03:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/727379"], "description"=>"<p>The firing rate responses of an 8×8 sample of fourth (output) layer cells are shown before training (left) and after training (right) with the full set of 1600 complete faces. Each of the 8×8 sub-plots represents an individual cell in the output layer, and shows its firing-rate profile to all 1600 faces presented during testing. For each subplot, the horizontal axis denotes the position of the test face within the identity space and the vertical axis denotes the position within the expression space. The responses of the neuron are represented on a grey scale where black indicates high firing. After training, some individual neurons learned to respond to local portions of their preferred space, either identity or expression, irrespective of the position in the alternative space. This results in the appearance of vertical or horizontal ‘bars’ of activation in the subplots. If a neuron responds selectively to a local region of the identity space, then this results in a vertical bar in the subplot. In contrast, if a neuron responds to a local region within the expression space, then this results in a horizontal bar. Furthermore, due to the effects of the SOM architecture, cells with similar response profiles form close together. This causes spatial clustering of cells that respond preferentially to either facial identity or facial expression.</p>", "links"=>[], "tags"=>["firing", "responses"], "article_id"=>397737, "categories"=>["Biological Sciences", "Neuroscience", "Mental Health"], "users"=>["James Matthew Tromans", "Mitchell Harris", "Simon Maitland Stringer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025616.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Neuronal_firing_rate_responses_to_complete_faces_/397737", "title"=>"Neuronal firing rate responses to complete faces.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:03:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/727876"], "description"=>"<p>Network dimensions showing the number of connections per neuron and the radius in the preceding layer from which 67% are received.</p>", "links"=>[], "tags"=>["Mental health", "Computational biology", "neuroscience"], "article_id"=>398234, "categories"=>["Biological Sciences", "Neuroscience", "Mental Health"], "users"=>["James Matthew Tromans", "Mitchell Harris", "Simon Maitland Stringer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025616.t001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_dimensions_/398234", "title"=>"Network dimensions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 14:05:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/727281"], "description"=>"<p>A selection of 16 cartoon face stimuli presented to VisNet where identity (represented by the shape and location of the eyes and nose) changes along the horizontal dimension (left to right) and expression (represented by the shape and location on the mouth and eyebrows) changes along the vertical dimension (top to bottom). A total of 1600 faces were produced by combining all possible combinations of the 40 identities and 40 expressions.</p>", "links"=>[], "tags"=>["Mental health", "Computational biology", "neuroscience"], "article_id"=>397638, "categories"=>["Biological Sciences", "Neuroscience", "Mental Health"], "users"=>["James Matthew Tromans", "Mitchell Harris", "Simon Maitland Stringer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025616.g001", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cartoon_face_stimuli_/397638", "title"=>"Cartoon face stimuli.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:02:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/727848"], "description"=>"<p>Lateral inhibition and excitation parameters for the SOM.</p>", "links"=>[], "tags"=>["connectivity"], "article_id"=>398210, "categories"=>["Biological Sciences", "Neuroscience", "Mental Health"], "users"=>["James Matthew Tromans", "Mitchell Harris", "Simon Maitland Stringer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025616.t003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Lateral_Connectivity_Parameters_/398210", "title"=>"Lateral Connectivity Parameters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 14:05:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/727432"], "description"=>"<p>First the network is trained with all 1600 complete faces. Then the network is tested with the visual features that represent either the 40 transforms of facial expression or the 40 transforms of facial identity presented separately. The figure displays the responses of four different kinds of typical output cells: (a) a cell that responds exclusively to a portion of the identity space; (b) a cell that responds exclusively to a portion of the expression space; (c) a cell that responds to both portions of the identity and expression space; and (d) a cell that responds to multiple portions of either or both spaces. Cell responses to the 40 transforms of identity are plotted with the solid line, and cell responses to the 40 transforms of expression are plotted with the dashed line. For each cell, the firing-rate (0–1) is plotted on the y-axis against the identity/expression number (1–40) on the x-axis.</p>", "links"=>[], "tags"=>["firing", "responses"], "article_id"=>397793, "categories"=>["Biological Sciences", "Neuroscience", "Mental Health"], "users"=>["James Matthew Tromans", "Mitchell Harris", "Simon Maitland Stringer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025616.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Neuronal_firing_rate_responses_to_either_identity_or_expression_/397793", "title"=>"Neuronal firing rate responses to either identity or expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:03:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/727740"], "description"=>"<p>Left: Stylised image of the four layer VisNet network. The four layers of the network represent successive stages in the ventral visual pathway of the primate brain: V2, V4, TEO (posterior inferior temporal cortex) and TE (anterior inferior temporal cortex). The synaptic connections to V2, the first layer of the network, are derived from an array of input filters with the response characteristics of V1 simple cells. The V1 input filters are used to process the raw visual images to provide input to the first layer of the network. The layer of V2 cells then sends feedforward connections to the V4 layer. Similarly, V4 sends feedforward connections to TEO, and TEO sends feedforward connections to TE. During training with visual images, the feedforward synaptic connections into each of the four stages (V2, V4, TEO and TE) are modified by Hebbian learning. Convergence through the network is designed to provide fourth layer (TE) neurons with information from across the entire input retina. Right: Convergence of feedforward connections through successive stages of the ventral visual pathway. The receptive field size of neurons increases through successive layers. The final layer, TE, receives visual input from across the whole retina.</p>", "links"=>[], "tags"=>["Mental health", "Computational biology", "neuroscience"], "article_id"=>398103, "categories"=>["Biological Sciences", "Neuroscience", "Mental Health"], "users"=>["James Matthew Tromans", "Mitchell Harris", "Simon Maitland Stringer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025616.g006", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_VisNet_model_/398103", "title"=>"VisNet model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:05:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/727816"], "description"=>"<p>The numbers of connections from each spatial frequency set of filters are shown. The spatial frequency is in cycles per pixel.</p>", "links"=>[], "tags"=>["Mental health", "Computational biology", "neuroscience"], "article_id"=>398177, "categories"=>["Biological Sciences", "Neuroscience", "Mental Health"], "users"=>["James Matthew Tromans", "Mitchell Harris", "Simon Maitland Stringer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025616.t002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Layer_1_connectivity_/398177", "title"=>"Layer 1 connectivity.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 14:05:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/727611"], "description"=>"<p>Analysis of information about identity conveyed by fourth (output) layer cells before and after training on 1600 complete faces. The information analysis was performed as described in the <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0025616#s4\" target=\"_blank\">Methods</a>. The plot shows the amount of single cell information carried by individual output cells in rank order. In the trained condition, 25 neurons provided maximal information of 2.32 bits. In the untrained condition, no cells reached maximal information.</p>", "links"=>[], "tags"=>["Mental health", "Computational biology", "neuroscience"], "article_id"=>397970, "categories"=>["Biological Sciences", "Neuroscience", "Mental Health"], "users"=>["James Matthew Tromans", "Mitchell Harris", "Simon Maitland Stringer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025616.g005", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Information_analysis_for_identity_/397970", "title"=>"Information analysis for identity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:04:18"}

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Relative Metric

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