Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network
Events
Loading … Spinner

Mendeley | Further Information

{"title"=>"Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network", "type"=>"journal", "authors"=>[{"first_name"=>"Oren", "last_name"=>"Shriki", "scopus_author_id"=>"6506619366"}, {"first_name"=>"Dovi", "last_name"=>"Yellin", "scopus_author_id"=>"24826029800"}], "year"=>2016, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-84959454686", "pmid"=>"26882372", "sgr"=>"84959454686", "issn"=>"15537358", "pui"=>"608854496", "isbn"=>"1553-734x", "doi"=>"10.1371/journal.pcbi.1004698"}, "id"=>"76b41561-4070-3a39-990b-0bf229c09fbd", "abstract"=>"Recurrent connections play an important role in cortical function, yet their exact contribution to the network computation remains unknown. The principles guiding the long-term evolution of these connections are poorly understood as well. Therefore, gaining insight into their computational role and into the mechanism shaping their pattern would be of great importance. To that end, we studied the learning dynamics and emergent recurrent connectivity in a sensory network model based on a first-principle information theoretic approach. As a test case, we applied this framework to a model of a hypercolumn in the visual cortex and found that the evolved connections between orientation columns have a \"Mexican hat\" profile, consistent with empirical data and previous modeling work. Furthermore, we found that optimal information representation is achieved when the network operates near a critical point in its dynamics. Neuronal networks working near such a phase transition are most sensitive to their inputs and are thus optimal in terms of information representation. Nevertheless, a mild change in the pattern of interactions may cause such networks to undergo a transition into a different regime of behavior in which the network activity is dominated by its internal recurrent dynamics and does not reflect the objective input. We discuss several mechanisms by which the pattern of interactions can be driven into this supercritical regime and relate them to various neurological and neuropsychiatric phenomena.", "link"=>"http://www.mendeley.com/research/optimal-information-representation-criticality-adaptive-sensory-recurrent-neuronal-network", "reader_count"=>39, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>3, "Researcher"=>7, "Student > Ph. D. Student"=>14, "Other"=>1, "Student > Master"=>6, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>3, "Researcher"=>7, "Student > Ph. D. Student"=>14, "Other"=>1, "Student > Master"=>6, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>3, "Mathematics"=>1, "Agricultural and Biological Sciences"=>10, "Medicine and Dentistry"=>1, "Neuroscience"=>11, "Physics and Astronomy"=>5, "Psychology"=>2, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>11}, "Physics and Astronomy"=>{"Physics and Astronomy"=>5}, "Psychology"=>{"Psychology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>10}, "Computer Science"=>{"Computer Science"=>4}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"United States"=>2, "United Kingdom"=>1, "France"=>1, "Germany"=>2}, "group_count"=>1}

Scopus | Further Information

{"@_fa"=>"true", "link"=>[{"@_fa"=>"true", "@ref"=>"self", "@href"=>"https://api.elsevier.com/content/abstract/scopus_id/84959454686"}, {"@_fa"=>"true", "@ref"=>"author-affiliation", "@href"=>"https://api.elsevier.com/content/abstract/scopus_id/84959454686?field=author,affiliation"}, {"@_fa"=>"true", "@ref"=>"scopus", "@href"=>"https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84959454686&origin=inward"}, {"@_fa"=>"true", "@ref"=>"scopus-citedby", "@href"=>"https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=84959454686&origin=inward"}], "prism:url"=>"https://api.elsevier.com/content/abstract/scopus_id/84959454686", "dc:identifier"=>"SCOPUS_ID:84959454686", "eid"=>"2-s2.0-84959454686", "dc:title"=>"Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network", "dc:creator"=>"Shriki O.", "prism:publicationName"=>"PLoS Computational Biology", "prism:issn"=>"1553734X", "prism:eIssn"=>"15537358", "prism:volume"=>"12", "prism:issueIdentifier"=>"2", "prism:pageRange"=>nil, "prism:coverDate"=>"2016-02-01", "prism:coverDisplayDate"=>"February 2016", "prism:doi"=>"10.1371/journal.pcbi.1004698", "citedby-count"=>"12", "affiliation"=>[{"@_fa"=>"true", "affilname"=>"Ben-Gurion University of the Negev", "affiliation-city"=>"Beer Sheba", "affiliation-country"=>"Israel"}], "pubmed-id"=>"26882372", "prism:aggregationType"=>"Journal", "subtype"=>"ar", "subtypeDescription"=>"Article", "article-number"=>"e1004698", "source-id"=>"4000151810", "openaccess"=>"1", "openaccessFlag"=>true}

Facebook

  • {"url"=>"http%3A%2F%2Fjournals.plos.org%2Fploscompbiol%2Farticle%3Fid%3D10.1371%252Fjournal.pcbi.1004698", "share_count"=>18, "like_count"=>40, "comment_count"=>9, "click_count"=>0, "total_count"=>67}

Twitter

Counter

  • {"month"=>"2", "year"=>"2016", "pdf_views"=>"143", "xml_views"=>"1", "html_views"=>"1574"}
  • {"month"=>"3", "year"=>"2016", "pdf_views"=>"83", "xml_views"=>"2", "html_views"=>"403"}
  • {"month"=>"4", "year"=>"2016", "pdf_views"=>"33", "xml_views"=>"1", "html_views"=>"175"}
  • {"month"=>"5", "year"=>"2016", "pdf_views"=>"36", "xml_views"=>"0", "html_views"=>"113"}
  • {"month"=>"6", "year"=>"2016", "pdf_views"=>"19", "xml_views"=>"0", "html_views"=>"88"}
  • {"month"=>"7", "year"=>"2016", "pdf_views"=>"24", "xml_views"=>"0", "html_views"=>"134"}
  • {"month"=>"8", "year"=>"2016", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"73"}
  • {"month"=>"9", "year"=>"2016", "pdf_views"=>"18", "xml_views"=>"0", "html_views"=>"44"}
  • {"month"=>"10", "year"=>"2016", "pdf_views"=>"13", "xml_views"=>"0", "html_views"=>"27"}
  • {"month"=>"11", "year"=>"2016", "pdf_views"=>"15", "xml_views"=>"0", "html_views"=>"47"}
  • {"month"=>"12", "year"=>"2016", "pdf_views"=>"8", "xml_views"=>"2", "html_views"=>"32"}
  • {"month"=>"1", "year"=>"2017", "pdf_views"=>"17", "xml_views"=>"0", "html_views"=>"48"}
  • {"month"=>"2", "year"=>"2017", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"52"}
  • {"month"=>"3", "year"=>"2017", "pdf_views"=>"12", "xml_views"=>"1", "html_views"=>"40"}
  • {"month"=>"4", "year"=>"2017", "pdf_views"=>"4", "xml_views"=>"0", "html_views"=>"29"}
  • {"month"=>"5", "year"=>"2017", "pdf_views"=>"14", "xml_views"=>"1", "html_views"=>"42"}
  • {"month"=>"6", "year"=>"2017", "pdf_views"=>"7", "xml_views"=>"1", "html_views"=>"19"}
  • {"month"=>"7", "year"=>"2017", "pdf_views"=>"13", "xml_views"=>"0", "html_views"=>"30"}
  • {"month"=>"8", "year"=>"2017", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"29"}
  • {"month"=>"9", "year"=>"2017", "pdf_views"=>"10", "xml_views"=>"1", "html_views"=>"37"}
  • {"month"=>"10", "year"=>"2017", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"29"}
  • {"month"=>"11", "year"=>"2017", "pdf_views"=>"16", "xml_views"=>"0", "html_views"=>"50"}
  • {"month"=>"12", "year"=>"2017", "pdf_views"=>"12", "xml_views"=>"2", "html_views"=>"19"}
  • {"month"=>"1", "year"=>"2018", "pdf_views"=>"6", "xml_views"=>"0", "html_views"=>"21"}
  • {"month"=>"2", "year"=>"2018", "pdf_views"=>"12", "xml_views"=>"1", "html_views"=>"14"}
  • {"month"=>"3", "year"=>"2018", "pdf_views"=>"3", "xml_views"=>"0", "html_views"=>"14"}
  • {"month"=>"4", "year"=>"2018", "pdf_views"=>"5", "xml_views"=>"0", "html_views"=>"34"}
  • {"month"=>"5", "year"=>"2018", "pdf_views"=>"8", "xml_views"=>"1", "html_views"=>"12"}
  • {"month"=>"6", "year"=>"2018", "pdf_views"=>"10", "xml_views"=>"0", "html_views"=>"17"}
  • {"month"=>"7", "year"=>"2018", "pdf_views"=>"12", "xml_views"=>"6", "html_views"=>"14"}
  • {"month"=>"8", "year"=>"2018", "pdf_views"=>"5", "xml_views"=>"3", "html_views"=>"10"}
  • {"month"=>"9", "year"=>"2018", "pdf_views"=>"16", "xml_views"=>"1", "html_views"=>"12"}
  • {"month"=>"10", "year"=>"2018", "pdf_views"=>"17", "xml_views"=>"2", "html_views"=>"15"}
  • {"month"=>"11", "year"=>"2018", "pdf_views"=>"4", "xml_views"=>"0", "html_views"=>"8"}
  • {"month"=>"12", "year"=>"2018", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"11"}
  • {"month"=>"1", "year"=>"2019", "pdf_views"=>"4", "xml_views"=>"0", "html_views"=>"11"}
  • {"month"=>"2", "year"=>"2019", "pdf_views"=>"5", "xml_views"=>"0", "html_views"=>"10"}
  • {"month"=>"3", "year"=>"2019", "pdf_views"=>"7", "xml_views"=>"4", "html_views"=>"15"}
  • {"month"=>"4", "year"=>"2019", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"13"}
  • {"month"=>"5", "year"=>"2019", "pdf_views"=>"5", "xml_views"=>"0", "html_views"=>"5"}

Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/3934219"], "description"=>"<p>Results of the toy model following gradient-descent learning that minimizes the objective function at a mean contrast of 〈<i>r</i>〉 = 0.1. (A) The optimal interaction matrix representing the strength of recurrent connections with gray level value (interaction from neuron <i>j</i> onto neuron <i>i</i> as grey level of the pixel in the <i>i’</i>th row and <i>j’</i>th column). (B) The interaction profile for the neuron tuned to 180° (the middle column of the interaction matrix). (C) Network response in presence and absence of recurrent interactions, for an input with contrast of <i>r</i> = 0.1. The dashed line is the response of the network without the recurrent interactions and the solid line is the response with them. (D) The network’s response amplification to inputs at different levels of contrast. (E-G) The effect of scaling the recurrent interactions on several metrics of network behavior. (E) Objective function. (F) Convergence time of the recurrent network. (G) Magnitude of the population vector of the network response. PO—preferred orientation.</p>", "links"=>[], "tags"=>["dynamic", "Adaptive Sensory Recurrent Neuronal Network Recurrent connections", "pattern", "Optimal Information Representation", "information representation"], "article_id"=>2296753, "categories"=>["Biophysics", "Neuroscience", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Mathematical Sciences not elsewhere classified"], "users"=>["Oren Shriki", "Dovi Yellin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004698.g002", "stats"=>{"downloads"=>0, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Behavior_of_the_simplified_hypercolumn_model_in_the_limit_of_low_input_contrast_/2296753", "title"=>"Behavior of the simplified hypercolumn model in the limit of low input contrast.", "pos_in_sequence"=>2, "defined_type"=>1, "published_date"=>"2016-02-16 13:59:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/3934204"], "description"=>"<p>(A) The general network architecture characterized by an overcomplete representation with <i>N</i> input neurons, <i>x</i><sub><i>i</i></sub> (<i>i</i> = 1,…,<i>N</i>) and <i>M ≥ N</i> output neurons, <i>s</i><sub><i>i</i></sub> (<i>i</i> = 1,…,<i>M</i>). The input is linearly transformed by the feedforward connection matrix,<b><i>W</i></b>, and then nonlinearly processed by the recurrent dynamics at the output layer. The interactions among the output neurons are denoted by the matrix <b><i>K</i></b>. (B) A toy network model of a visual hypercolumn containing 2 input neurons and <i>M</i> output neurons. The feedforward connections are preset to unit vectors spanning all angles at equal intervals. The inputs are points on the plane with uniformly distributed angles and normally distributed distances from the origin. The distance from the origin represents the input contrast. (C) An ecological network model in which the inputs are natural images. The feedforward connections are Gabor filters with orientations equally spaced between 0° and 180°.</p>", "links"=>[], "tags"=>["dynamic", "Adaptive Sensory Recurrent Neuronal Network Recurrent connections", "pattern", "Optimal Information Representation", "information representation"], "article_id"=>2296738, "categories"=>["Biophysics", "Neuroscience", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Mathematical Sciences not elsewhere classified"], "users"=>["Oren Shriki", "Dovi Yellin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004698.g001", "stats"=>{"downloads"=>0, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Network_diagrams_and_input_statistics_/2296738", "title"=>"Network diagrams and input statistics.", "pos_in_sequence"=>1, "defined_type"=>1, "published_date"=>"2016-02-16 13:59:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/3934267"], "description"=>"<p>The figure is organized similarly to <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004698#pcbi.1004698.g002\" target=\"_blank\">Fig 2</a>. (A) Interaction matrix. (B) Interaction profile. (C) Network response with and without recurrent interactions to an oriented stimulus (a Gabor filter with similar properties to the preset feedforward filter). (D) Response amplification at different contrast levels. (E-G) Objective function (E), convergence time (F) and magnitude of the population vector (G) as a function of the scale of the recurrent interactions. Notably, the evolved network dynamics when exposed to natural images resembles more closely the behavior of the toy model after training with low contrast stimuli. PO—preferred orientation.</p>", "links"=>[], "tags"=>["dynamic", "Adaptive Sensory Recurrent Neuronal Network Recurrent connections", "pattern", "Optimal Information Representation", "information representation"], "article_id"=>2296798, "categories"=>["Biophysics", "Neuroscience", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Mathematical Sciences not elsewhere classified"], "users"=>["Oren Shriki", "Dovi Yellin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004698.g004", "stats"=>{"downloads"=>1, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Behavior_of_the_ecological_hypercolumn_model_after_training_with_natural_scenes_/2296798", "title"=>"Behavior of the ecological hypercolumn model after training with natural scenes.", "pos_in_sequence"=>4, "defined_type"=>1, "published_date"=>"2016-02-16 13:59:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/3934246"], "description"=>"<p>Results of the toy model following gradient-descent learning that minimizes the objective function at a mean contrast of 〈<i>r</i> 〉 = 0.9. The figure is organized similarly to <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004698#pcbi.1004698.g002\" target=\"_blank\">Fig 2</a>. (A) Interaction matrix. (B) Interaction profile. (C) Network response with and without recurrent interactions. (D) Response amplification at different contrast levels. (E-G) Objective function (E), convergence time (F) and magnitude of the population vector (G) as a function of the scale of the recurrent interactions. Relative to the case of learning in the domain of low input contrasts, here, recurrent interactions are less crucial for performance. PO—preferred orientation.</p>", "links"=>[], "tags"=>["dynamic", "Adaptive Sensory Recurrent Neuronal Network Recurrent connections", "pattern", "Optimal Information Representation", "information representation"], "article_id"=>2296777, "categories"=>["Biophysics", "Neuroscience", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Mathematical Sciences not elsewhere classified"], "users"=>["Oren Shriki", "Dovi Yellin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004698.g003", "stats"=>{"downloads"=>1, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Behavior_of_the_simplified_hypercolumn_model_for_intermediate_input_contrast_/2296777", "title"=>"Behavior of the simplified hypercolumn model for intermediate input contrast.", "pos_in_sequence"=>3, "defined_type"=>1, "published_date"=>"2016-02-16 13:59:02"}

PMC Usage Stats | Further Information

  • {"unique-ip"=>"14", "full-text"=>"10", "pdf"=>"11", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"3"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"4"}
  • {"unique-ip"=>"8", "full-text"=>"5", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"5"}
  • {"unique-ip"=>"14", "full-text"=>"8", "pdf"=>"8", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"6"}
  • {"unique-ip"=>"3", "full-text"=>"4", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"7"}
  • {"unique-ip"=>"4", "full-text"=>"5", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"8"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"9"}
  • {"unique-ip"=>"9", "full-text"=>"7", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"10"}
  • {"unique-ip"=>"7", "full-text"=>"4", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"5", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"11"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"12"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"1"}
  • {"unique-ip"=>"1", "full-text"=>"0", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"2"}
  • {"unique-ip"=>"5", "full-text"=>"8", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"3"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"4"}
  • {"unique-ip"=>"3", "full-text"=>"2", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"5"}
  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"6"}
  • {"unique-ip"=>"6", "full-text"=>"7", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"7"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"8"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"9"}
  • {"unique-ip"=>"4", "full-text"=>"5", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"10"}
  • {"unique-ip"=>"13", "full-text"=>"8", "pdf"=>"5", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2017", "month"=>"11"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"12"}
  • {"unique-ip"=>"3", "full-text"=>"6", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"1"}
  • {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"2", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
  • {"unique-ip"=>"4", "full-text"=>"2", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
  • {"unique-ip"=>"2", "full-text"=>"0", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"1", "full-text"=>"2", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}

Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
Loading … Spinner
There are currently no alerts