Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics
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{"title"=>"Cortical surround interactions and perceptual salience via natural scene statistics", "type"=>"journal", "authors"=>[{"first_name"=>"Ruben", "last_name"=>"Coen-Cagli", "scopus_author_id"=>"10045769900"}, {"first_name"=>"Peter", "last_name"=>"Dayan", "scopus_author_id"=>"7006914663"}, {"first_name"=>"Odelia", "last_name"=>"Schwartz", "scopus_author_id"=>"7006284113"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84861108659", "doi"=>"10.1371/journal.pcbi.1002405", "pui"=>"364830944", "pmid"=>"22396635", "scopus"=>"2-s2.0-84861108659", "issn"=>"1553734X", "isbn"=>"1553-7358"}, "id"=>"d6f47493-b488-3df3-8269-73a90c33d97c", "abstract"=>"Spatial context in images induces perceptual phenomena associated with salience and modulates the responses of neurons in primary visual cortex (V1). However, the computational and ecological principles underlying contextual effects are incompletely understood. We introduce a model of natural images that includes grouping and segmentation of neighboring features based on their joint statistics, and we interpret the firing rates of V1 neurons as performing optimal recognition in this model. We show that this leads to a substantial generalization of divisive normalization, a computation that is ubiquitous in many neural areas and systems. A main novelty in our model is that the influence of the context on a target stimulus is determined by their degree of statistical dependence. We optimized the parameters of the model on natural image patches, and then simulated neural and perceptual responses on stimuli used in classical experiments. The model reproduces some rich and complex response patterns observed in V1, such as the contrast dependence, orientation tuning and spatial asymmetry of surround suppression, while also allowing for surround facilitation under conditions of weak stimulation. It also mimics the perceptual salience produced by simple displays, and leads to readily testable predictions. Our results provide a principled account of orientation-based contextual modulation in early vision and its sensitivity to the homogeneity and spatial arrangement of inputs, and lends statistical support to the theory that V1 computes visual salience.", "link"=>"http://www.mendeley.com/research/cortical-surround-interactions-perceptual-salience-via-natural-scene-statistics", "reader_count"=>133, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>11, "Researcher"=>38, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>43, "Student > Postgraduate"=>5, "Student > Master"=>15, "Other"=>3, "Student > Bachelor"=>4, "Professor"=>7}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>11, "Researcher"=>38, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>43, "Student > Postgraduate"=>5, "Student > Master"=>15, "Other"=>3, "Student > Bachelor"=>4, "Professor"=>7}, "reader_count_by_subject_area"=>{"Engineering"=>10, "Unspecified"=>6, "Mathematics"=>1, "Medicine and Dentistry"=>6, "Agricultural and Biological Sciences"=>47, "Neuroscience"=>20, "Philosophy"=>2, "Physics and Astronomy"=>4, "Psychology"=>21, "Computer Science"=>16}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>10}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Neuroscience"=>{"Neuroscience"=>20}, "Physics and Astronomy"=>{"Physics and Astronomy"=>4}, "Psychology"=>{"Psychology"=>21}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>47}, "Computer Science"=>{"Computer Science"=>16}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>6}, "Philosophy"=>{"Philosophy"=>2}}, "reader_count_by_country"=>{"Canada"=>2, "Netherlands"=>1, "United States"=>13, "United Kingdom"=>1, "Italy"=>1, "France"=>1, "Switzerland"=>2, "Germany"=>4}, "group_count"=>5}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/673761"], "description"=>"<p>(<b>A–C</b>) V1 population data from <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi.1002405-Cavanaugh2\" target=\"_blank\">[9]</a>, N = 113. (<b>D–F</b>) model simulations for 4 center orientations, 2 contrasts (0.25, 0.5) and 39 parameter sets resulting from different natural image training sets (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#s2\" target=\"_blank\">Materials and Methods</a>). (<b>A,D</b>) were obtained with the surround stimuli of <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi-1002405-g009\" target=\"_blank\">Fig. 9</a>. The magnitude of the suppressive effect and surround location of greatest suppression are plotted in polar coordinates (duplicated around the circle for visualization): points farther from the origin correspond to stronger suppression than those closer to the origin. The location of maximal suppression is the angle of the vector computed from the response reduction, as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi.1002405-Cavanaugh2\" target=\"_blank\">[9]</a>. Black circles represent cases with a strong effect (i.e. the magnitude of the orientation bias estimate was at least 0.2 and the maximum suppression was at least 0.3; 42/113 cells in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi.1002405-Cavanaugh2\" target=\"_blank\">[9]</a> and 83/272 instances of the model matched both criteria). The gray line shows the angle estimate for the cluster of such points. (<b>B,E</b>) were obtained with surround stimuli presented in the same locations as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi-1002405-g009\" target=\"_blank\">Fig. 9</a>, but oriented orthogonally to the center (see also <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi.1002405.s007\" target=\"_blank\">Text S4</a>). All conventions are the same as in (<b>A,D</b>). The effect was strong in 33/113 cells in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi.1002405-Cavanaugh2\" target=\"_blank\">[9]</a>, and 106/272 model instances. (<b>C,F</b>) represent, only for the cases with a strong effect, the distribution of the difference between the most suppressive surround location and the axis of preferred orientation.</p>", "links"=>[], "tags"=>["positional"], "article_id"=>344254, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ruben Coen-Cagli", "Peter Dayan", "Odelia Schwartz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002405.g010", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variability_of_positional_biases_/344254", "title"=>"Variability of positional biases.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-01 01:10:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/673455"], "description"=>"<p>(<b>A</b>) Probability for the vertical center and surround RFs as a function of grating contrast. The stimulus diameter is 7 pixels. All the RFs have diameter 9 pixels, and the mid point of each surround RF is located 6 pixels away from the mid point of the center RF, so a stimulus 7 pixels wide encroaches on the surround RFs. Although it does so only by 2 pixels, in the simulations this leads to a large co-assignment probability at high contrast; we have verified that this is not the case for a stimulus 5 pixel wide (in which case the overlap with surround RFs is even smaller, and not enough to recruit the surround). (<b>B</b>) Probability for the vertical center and surround RFs on natural images. The X axis represents values of for vertical RFs (<b>k</b>, <b>S</b>), with covariance matrix . The term therefore increases for larger values of the RF outputs (note that for any fixed image, scaling the contrast by a factor <i>c</i> also scales by <i>c</i>). For each input image, we computed and the co-assignment probability for the configuration ; the Y axis represents the mean of such probability across the inputs corresponding to given values of . The dashed line corresponds to the prior probability learned by the model on natural images.</p>", "links"=>[], "tags"=>["probability", "depends"], "article_id"=>343943, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ruben Coen-Cagli", "Peter Dayan", "Odelia Schwartz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002405.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Co_assignment_probability_depends_on_image_contrast_/343943", "title"=>"Co-assignment probability depends on image contrast.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-01 01:05:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/673612"], "description"=>"<p>(<b>A</b>) Normalized mean response rate of V1 neurons (top and bottom left; <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi.1002405-Jones1\" target=\"_blank\">[10]</a>, their <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi-1002405-g001\" target=\"_blank\">Fig. 1A</a> and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi-1002405-g004\" target=\"_blank\">4D</a>, respectively) and the model (right) to mid contrast stimuli comprising an optimally oriented grating presented to the RF center, and an annular grating of variable orientation in the surround. The insets show some example stimuli. The outer diameter of the annular patches used to test the model covers the full extent of the surround, and the inner diameter (11 pixels) is larger than the center RF to ensure that the surround stimulus does not encroach on the center. In the top-right panel, the center stimulus equals the center RF diameter (9 pixels), in bottom-right panel it is smaller (5 pixels). Responses (circles) are plotted as a function of the difference between center and surround orientation. The dashed line denotes the response to an optimal grating patch not surrounded by an annulus. (<b>B</b>) Probability that center RFs and surround RFs of each orientation (colors indicated in the legend) are co-assigned to the same normalization pool; the stimuli are the same as in (<b>A</b>), and some examples are depicted in the icons. The bounding circle (dashed line) represents a probability of 1. Probabilities are plotted in polar coordinates: angular position represents the orientation of the surround stimulus; distance from the origin represents the probability. (<b>C</b>) Circles and thick lines: Normalized mean response rate of a V1 neuron (left; <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi.1002405-Cavanaugh2\" target=\"_blank\">[9]</a>) and the model (right) to stimuli similar to (<b>A</b>), except that the central grating is tilted 45 degrees away from the neuron's preference. Thin lines: Orientation tuning curves measured with a small grating. (<b>D</b>) Probability that center RFs and surround RFs of each orientation are co-assigned. The conventions are the same as in (<b>B</b>). (<b>E</b>) Orientation tuning curves of a V1 neuron (left; <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi.1002405-Chen1\" target=\"_blank\">[11]</a>) and the model (right) measured with small gratings confined to the center RF (thin line), and large gratings covering also the surround (thick line and circles): narrower tuning curves are observed with large stimuli. Each curve is normalized by the response to the optimal orientation. In the experiment, the diameter of large gratings is 5 times the center RF size; in our simulations, we used large gratings that cover the full surround extent. (<b>F</b>) Probability that center RFs and surround RFs of each orientation are co-assigned, for the large gratings. For each stimulus orientation, the surround group with orientation closest to the stimulus is co-assigned with center RFs.</p>", "links"=>[], "tags"=>["tuning"], "article_id"=>344104, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ruben Coen-Cagli", "Peter Dayan", "Odelia Schwartz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002405.g008", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Orientation_tuning_of_surround_modulation_/344104", "title"=>"Orientation tuning of surround modulation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-01 01:08:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/673375"], "description"=>"<p>Top row: co-assigned components (from left to right, ); bottom row: independent component (). Black bars denote the orientation and relative position of the RFs; bar thickness is proportional to the variance. The thickness of the red lines connecting pairs of bars is proportional to the absolute value of the covariance. For each surround group, we show only the covariance with the center of the same orientation; the covariance with center RFs of different orientations is one to three orders of magnitude weaker. Similarly, we show only the covariances between RFs with even phase; the covariances for the odd phase are similar, while those across different phases are negligible. In each mixture component the variances of the center RF and its collinear neighbors, as well as the covariance between them, are larger reflecting the predominance of collinear structure in scenes.</p>", "links"=>[], "tags"=>["covariance", "matrices", "learned", "scenes", "gaussian", "variables", "rf", "outputs", "components"], "article_id"=>343861, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ruben Coen-Cagli", "Peter Dayan", "Odelia Schwartz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002405.g004", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Visualization_of_the_covariance_matrices_learned_from_scenes_between_the_Gaussian_variables_associated_with_center_and_surround_RF_outputs_in_the_mixture_components_of_Fig_3_/343861", "title"=>"Visualization of the covariance matrices learned from scenes between the Gaussian variables associated with center and surround RF outputs in the mixture components of <b>Fig. 3</b>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-01 01:04:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/673233"], "description"=>"<p>(<b>A–D</b>) Histograms of the outputs of one RF (<i>k</i><sub>1</sub>) given the outputs of the other RF (<i>k</i><sub>2</sub>). We computed these conditional histograms based on 100,000 Gaussian white noise image patches (<b>A,B</b>), or 100,000 natural image patches (<b>C,D</b>; the images are shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi.1002405.s001\" target=\"_blank\">Fig. S1</a>). Pixel intensity is proportional to probability, larger values correspond to brighter pixels; we rescaled each column independently to fill the range of intensities. Solid and dashed lines denote the mean and standard deviation, respectively, of <i>k</i><sub>1</sub> for each given value of <i>k</i><sub>2</sub>. We matched the average RMS contrast of noise and natural images; the larger range of RFs responses to natural images reflects the abundance of oriented features that are optimal for the RFs. The insets illustrate the orientation and relative position of the RFs: (<b>A</b>) collinear RFs with large overlap (3 pixels separation); (<b>B,D</b>) collinear, and (<b>C</b>) parallel but not collinear RFs, with minimal overlap (6 pixel separation). The <i>bowtie</i> shape in (<b>C,D</b>) shows that the variance of <i>k</i><sub>1</sub> depends on the magnitude of <i>k</i><sub>2</sub>, which is typical of natural images. Further, we report the Pearson correlation coefficient between <i>k</i><sub>1</sub> and <i>k</i><sub>2</sub> at the bottom: the stronger linear dependence between collinear filters reflects the predominance of elongated edges and contours in scenes. (<b>E,F</b>) Histograms of the outputs of two spatially separated vertical RFs, at center (Y axis) and surround (X axis) locations, averaged across 8 surround locations as illustrated in the axis labels (black bars denote filters, the red cross denotes the center position; surround RFs are 6 pixels away from the center). In (<b>E</b>) we included only the subset of the patches in (<b>C,D</b>) that were best described by a model with statistically dependent center and vertical surround RFs; whereas in (<b>F</b>) we used the patches best described by a model that assumes independence between center and surround RFs (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#s2\" target=\"_blank\">Materials and Methods</a> for model details). The variance dependence is weaker in (<b>F</b>) than (<b>E</b>).</p>", "links"=>[], "tags"=>["amongst", "oriented", "filters", "spatial"], "article_id"=>343718, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ruben Coen-Cagli", "Peter Dayan", "Odelia Schwartz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002405.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dependencies_amongst_oriented_filters_vary_with_spatial_layout_image_set_and_across_image_regions_/343718", "title"=>"Dependencies amongst oriented filters vary with spatial layout, image set, and across image regions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-01 01:01:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/673552"], "description"=>"<p>(<b>A</b>) Normalized mean response rate of a V1 neuron (left; <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi.1002405-Cavanaugh1\" target=\"_blank\">[8]</a>) and the model (right) as a function of stimulus size at two contrasts. The largest size we used to test the model covers the full extent of the surround RFs, and larger sizes will not change RFs outputs; for the 5 largest sizes tested, we observed little change in surround RFs outputs and model responses. Stimulus orientation and spatial frequency are optimal for the neuron. The insets show some example stimuli. The arrows (gray, low contrast; black, high contrast) indicate peak response diameter - or RF size. (<b>B</b>) Probability that center and vertical surround RFs are co-assigned to the same normalization pool, and therefore contribute to the divisive normalization of the model response; the stimuli are the same as in (<b>A</b>). At the smallest non-zero size, surround RFs are silent and therefore the surround is not co-assigned. At intermediate sizes, surround RFs outputs are weaker than center RFs outputs and co-assignment probability increases with contrast, as illustrated in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi-1002405-g005\" target=\"_blank\">Fig. 5</a>. At the largest sizes, RFs outputs are similar between center and surround and the surround is co-assigned.</p>", "links"=>[], "tags"=>["receptive"], "article_id"=>344046, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ruben Coen-Cagli", "Peter Dayan", "Odelia Schwartz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002405.g007", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Expansion_of_receptive_field_size_at_low_contrast_/344046", "title"=>"Expansion of receptive field size at low contrast.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-01 01:07:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/673903"], "description"=>"<p>(<b>A,B</b>) Illustration of the collinear enhancement of salience. Left: Original stimulus. Bars are 5 pixels long and separated by 6 pixels (equal to RFs spacing; see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#s2\" target=\"_blank\">Materials and Methods</a>). Center: salience map (pixel intensity codes for salience, with brighter pixels denoting higher salience). Symbols on the map identify regions of homogeneous texture (<i>Txt</i>, green), and the boundary regions formed by collinear (<i>Col</i>, red) or parallel (<i>Par</i>, blue) bars. Right: salience enhancement (Y axis) is quantified as <i>(Col-Txt)/Txt</i> (red line and symbols, for the collinear side) and <i>(Par-Txt)/Txt</i> (blue line and symbols, for the parallel side); the enhancement is 0 by definition for the homogeneous texture regions (green line and symbols). On the X axis, <i>Full</i> stands for the results obtained with the full model, whereas <i>Diag</i> stands for a reduced model that sets the covariance between center and surround RFs proportional to the identity matrix, rather than learning it from scenes. (<b>C</b>) Illustration of the border effect. All conventions are the same as in (<b>A,B</b>).</p>", "links"=>[], "tags"=>["saliency", "collinear"], "article_id"=>344399, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ruben Coen-Cagli", "Peter Dayan", "Odelia Schwartz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002405.g012", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Perceptual_saliency_of_collinear_stimuli_/344399", "title"=>"Perceptual saliency of collinear stimuli.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-01 01:13:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/673301"], "description"=>"<p>We used a bank of linear filters, depicted as colored bars in the top row, comprising 4 orientations at 1 center position and 8 surround positions. We grouped surround RFs according to their orientation, each labeled with a different color. A surround group can be either co-assigned with the center group (i.e., the model assumes dependence between center and surround groups, and includes them both in the normalization pool for the center, as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi-1002405-g002\" target=\"_blank\">Fig. 2-top</a>), or not co-assigned (i.e. the model assumes independence between center and surround groups, and does not include the latter in the normalization pool, as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi-1002405-g002\" target=\"_blank\">Fig. 2-bottom</a>). The leftmost column depicts the configuration (denoted by ) in which none of the surrounds is co-assigned with the center; best describes image patches such as those identified by the circles in the bottom row (red and orange circles denote center and surround respectively, as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi-1002405-g001\" target=\"_blank\">Fig. 1</a>). The second column depicts the configuration (denoted by ) in which the vertical surround is co-assigned with the center; black bars (top) identify the co-assigned groups, circles (bottom) the image patches best described by . The same conventions are used in the remaining columns.</p>", "links"=>[], "tags"=>["configurations", "corresponding", "components"], "article_id"=>343792, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ruben Coen-Cagli", "Peter Dayan", "Odelia Schwartz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002405.g003", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Center_surround_configurations_corresponding_to_the_different_mixture_components_of_the_model_/343792", "title"=>"Center-surround configurations corresponding to the different mixture components of the model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-01 01:03:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/673842"], "description"=>"<p>(<b>A</b>) Example stimulus (left) and the corresponding salience map (right). Bars are 5 pixels long (i.e., smaller than RFs) and separated by 6 pixels (equal to RFs spacing; see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#s2\" target=\"_blank\">Materials and Methods</a>). Pixel intensity codes for salience, with brighter pixels denoting higher salience. (<b>B</b>) Mean perceived luminance <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi.1002405-Nothdurft1\" target=\"_blank\">[3]</a> of a target defined by orientation-contrast, such as the vertical bar in the icon on (<b>A</b>, left). Perceived luminance is defined as the luminance of a luminance-contrast target (i.e. a bar with the same orientation as the background distractors, but higher luminance), required for it to be perceived as salient as the orientation-contrast target. Values are rescaled to a maximum of 1; the relative perceived luminance of the background bars is 0.36. (<b>C</b>) Saliency computed by the model for the orientation-contrast targets. Values are rescaled to a maximum of 1; the relative saliency of the background bars is 0.51.</p>", "links"=>[], "tags"=>["pop-out"], "article_id"=>344334, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ruben Coen-Cagli", "Peter Dayan", "Odelia Schwartz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002405.g011", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Perceptual_pop_out_in_a_population_of_model_neurons_/344334", "title"=>"Perceptual pop-out in a population of model neurons.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-01 01:12:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/673167"], "description"=>"<p>Top row: cartoon of a divisive normalization model that accounts for surround modulation of V1 responses. In a textured, homogeneous visual stimulus, the center and surround of a V1 neuron's RF (schematically illustrated by red and orange circles, respectively) receive similar inputs. The model pools together the corresponding outputs (computed by oriented linear filters), and combines them (here generically denoted by a function <i>h</i>; see Equations 4,6) to generate the signal that divisively normalizes the center output. Bottom row: cartoon of a divisive normalization model that accounts for the absence of surround modulation on heterogeneous visual stimuli (i.e., different features or textures stimulate the center and surround). The model uses only the center outputs to compute the normalization signal (see Equations 5,6).</p>", "links"=>[], "tags"=>["divisive"], "article_id"=>343658, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ruben Coen-Cagli", "Peter Dayan", "Odelia Schwartz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002405.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Stimulus_dependent_divisive_normalization_/343658", "title"=>"Stimulus-dependent divisive normalization.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-01 01:00:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/673498"], "description"=>"<p>Modulation of simulated model responses when the surround is co-assigned (<i>R<sub>co-assign</sub></i>; e.g. with a large, homogeneous grating) relative to when it is not co-assigned (<i>R<sub>no-assign</sub></i>; e.g. with a grating smaller than the central RF). Percent surround modulation is computed as 100*(<i>R<sub>co-assign</sub>−R<sub>no-assign</sub></i>)/<i>R<sub>no-assign</sub></i>. This quantity depends on RFs outputs only via and . Since appears in the denominator of Equation 3, the larger the the smaller the model response. We choose three representative values of (note that increases with larger center RF output). Surround modulation in the model can facilitate center responses for weak surrounds ( comparable with ), and gradually switch to suppression as surround strength (and therefore ) increases (relative to ). No surround modulation (dashed line) is observed e.g. with small stimuli or large, non-homogeneous stimuli for which center and surround RFs are not co-assigned.</p>", "links"=>[], "tags"=>["encompasses", "suppression"], "article_id"=>343992, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ruben Coen-Cagli", "Peter Dayan", "Odelia Schwartz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002405.g006", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_model_encompasses_both_surround_suppression_and_facilitation_/343992", "title"=>"The model encompasses both surround suppression and facilitation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-01 01:06:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/673690"], "description"=>"<p>(<b>A</b>) Normalized mean response rate of an example V1 neuron (left; <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#pcbi.1002405-Cavanaugh2\" target=\"_blank\">[9]</a>) and the model (right), to optimally oriented stimuli comprising a grating presented to the center of the RF, and peripheral patches confined to specific regions of the surround. Icons depict the stimulus configurations. The bounding circle represents the normalized response to the center stimulus alone. Responses are plotted in polar coordinates: angular position represents the location of the surround stimulus; distance from the origin represents the magnitude of response. In the experiments the size of the surround stimuli is optimized individually for each cell. For the model, we used annular sectors, rather than circular patches: first, this allowed us to explore systematically the changes in responses as a function of the surround stimulus angular size (thus making predictions beyond the original experiment); and, second, this was needed to recruit a sufficient number of surround RFs, given the coarse spatial sampling of the surround (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002405#s2\" target=\"_blank\">Materials and Methods</a>). (<b>B,C</b>) Suppression ratio (coded by pixel intensity) as a function of stimulus contrast and angular size of the peripheral patches (<b>B</b> collinear; <b>C</b> parallel). Suppression ratio is the ratio between the full-stimulus response and the response to the central grating alone; values greater than 1 denote facilitation, smaller than 1, suppression. (<b>D</b>) Difference of suppression ratios (coded by pixel intensity) between collinear and parallel configurations. Negative values imply that the collinear configuration is more suppressive than the orthogonal, and vice versa for positive values; the red contour denotes zero-crossing. (<b>E</b>) Probability that center and vertical surround RFs are co-assigned to the same normalization pool, and therefore contribute to the divisive normalization of the model response, for the stimuli of (<b>A</b>). (<b>F,G</b>) Co-assignment probability (coded by pixel intensity) as a function of stimulus contrast and angular size of the peripheral patches (<b>F</b> collinear; <b>G</b> parallel).</p>", "links"=>[], "tags"=>["asymmetry"], "article_id"=>344182, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ruben Coen-Cagli", "Peter Dayan", "Odelia Schwartz"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002405.g009", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_asymmetry_of_surround_modulation_/344182", "title"=>"Spatial asymmetry of surround modulation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-01 01:09:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/346704", "https://ndownloader.figshare.com/files/346763", "https://ndownloader.figshare.com/files/346816", "https://ndownloader.figshare.com/files/346876", "https://ndownloader.figshare.com/files/346940", "https://ndownloader.figshare.com/files/347087", "https://ndownloader.figshare.com/files/347732"], "description"=>"<div><p>Spatial context in images induces perceptual phenomena associated with salience and modulates the responses of neurons in primary visual cortex (V1). However, the computational and ecological principles underlying contextual effects are incompletely understood. We introduce a model of natural images that includes grouping and segmentation of neighboring features based on their joint statistics, and we interpret the firing rates of V1 neurons as performing optimal recognition in this model. We show that this leads to a substantial generalization of divisive normalization, a computation that is ubiquitous in many neural areas and systems. A main novelty in our model is that the influence of the context on a target stimulus is determined by their degree of statistical dependence. We optimized the parameters of the model on natural image patches, and then simulated neural and perceptual responses on stimuli used in classical experiments. The model reproduces some rich and complex response patterns observed in V1, such as the contrast dependence, orientation tuning and spatial asymmetry of surround suppression, while also allowing for surround facilitation under conditions of weak stimulation. It also mimics the perceptual salience produced by simple displays, and leads to readily testable predictions. Our results provide a principled account of orientation-based contextual modulation in early vision and its sensitivity to the homogeneity and spatial arrangement of inputs, and lends statistical support to the theory that V1 computes visual salience.</p> </div>", "links"=>[], "tags"=>["cortical", "interactions", "perceptual", "salience"], "article_id"=>128543, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ruben Coen-Cagli", "Peter Dayan", "Odelia Schwartz"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002405.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002405.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002405.s003", "https://dx.doi.org/10.1371/journal.pcbi.1002405.s004", "https://dx.doi.org/10.1371/journal.pcbi.1002405.s005", "https://dx.doi.org/10.1371/journal.pcbi.1002405.s006", "https://dx.doi.org/10.1371/journal.pcbi.1002405.s007"], "stats"=>{"downloads"=>11, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Cortical_Surround_Interactions_and_Perceptual_Salience_via_Natural_Scene_Statistics/128543", "title"=>"Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-03-01 02:22:23"}

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Relative Metric

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