Real-Time Parallel Processing of Grammatical Structure in the Fronto-Striatal System: A Recurrent Network Simulation Study Using Reservoir Computing
Events
Loading … Spinner

Mendeley | Further Information

{"title"=>"Real-Time Parallel Processing of Grammatical Structure in the Fronto-Striatal System: A Recurrent Network Simulation Study Using Reservoir Computing", "type"=>"journal", "authors"=>[{"first_name"=>"Xavier", "last_name"=>"Hinaut", "scopus_author_id"=>"53063834600"}, {"first_name"=>"Peter Ford", "last_name"=>"Dominey", "scopus_author_id"=>"7004714357"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"368254014", "arxiv"=>"1506.03229", "issn"=>"19326203", "isbn"=>"1932-6203", "doi"=>"10.1371/journal.pone.0052946", "scopus"=>"2-s2.0-84873259375", "pmid"=>"23383296", "sgr"=>"84873259375"}, "id"=>"e692c412-6939-36c9-b021-19d4b9782148", "abstract"=>"Sentence processing takes place in real-time. Previous words in the sentence can influence the processing of the current word in the timescale of hundreds of milliseconds. Recent neurophysiological studies in humans suggest that the fronto-striatal system (frontal cortex, and striatum--the major input locus of the basal ganglia) plays a crucial role in this process. The current research provides a possible explanation of how certain aspects of this real-time processing can occur, based on the dynamics of recurrent cortical networks, and plasticity in the cortico-striatal system. We simulate prefrontal area BA47 as a recurrent network that receives on-line input about word categories during sentence processing, with plastic connections between cortex and striatum. We exploit the homology between the cortico-striatal system and reservoir computing, where recurrent frontal cortical networks are the reservoir, and plastic cortico-striatal synapses are the readout. The system is trained on sentence-meaning pairs, where meaning is coded as activation in the striatum corresponding to the roles that different nouns and verbs play in the sentences. The model learns an extended set of grammatical constructions, and demonstrates the ability to generalize to novel constructions. It demonstrates how early in the sentence, a parallel set of predictions are made concerning the meaning, which are then confirmed or updated as the processing of the input sentence proceeds. It demonstrates how on-line responses to words are influenced by previous words in the sentence, and by previous sentences in the discourse, providing new insight into the neurophysiology of the P600 ERP scalp response to grammatical complexity. This demonstrates that a recurrent neural network can decode grammatical structure from sentences in real-time in order to generate a predictive representation of the meaning of the sentences. This can provide insight into the underlying mechanisms of human cortico-striatal function in sentence processing.", "link"=>"http://www.mendeley.com/research/realtime-parallel-processing-grammatical-structure-frontostriatal-system-recurrent-network-simulatio", "reader_count"=>80, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Researcher"=>19, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>3, "Student > Master"=>8, "Other"=>4, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Researcher"=>19, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>3, "Student > Master"=>8, "Other"=>4, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Agricultural and Biological Sciences"=>8, "Arts and Humanities"=>2, "Business, Management and Accounting"=>1, "Computer Science"=>20, "Engineering"=>6, "Materials Science"=>1, "Medicine and Dentistry"=>2, "Neuroscience"=>6, "Physics and Astronomy"=>7, "Psychology"=>12, "Social Sciences"=>3, "Linguistics"=>9}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Social Sciences"=>{"Social Sciences"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>7}, "Psychology"=>{"Psychology"=>12}, "Unspecified"=>{"Unspecified"=>3}, "Arts and Humanities"=>{"Arts and Humanities"=>2}, "Engineering"=>{"Engineering"=>6}, "Neuroscience"=>{"Neuroscience"=>6}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>8}, "Computer Science"=>{"Computer Science"=>20}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Linguistics"=>{"Linguistics"=>9}}, "reader_count_by_country"=>{"Colombia"=>1, "Canada"=>1, "Belgium"=>1, "United States"=>2, "Luxembourg"=>1, "United Kingdom"=>1, "France"=>3, "Germany"=>2, "India"=>1, "Spain"=>1}, "group_count"=>5}

Scopus | Further Information

{"@_fa"=>"true", "link"=>[{"@_fa"=>"true", "@ref"=>"self", "@href"=>"https://api.elsevier.com/content/abstract/scopus_id/84873259375"}, {"@_fa"=>"true", "@ref"=>"author-affiliation", "@href"=>"https://api.elsevier.com/content/abstract/scopus_id/84873259375?field=author,affiliation"}, {"@_fa"=>"true", "@ref"=>"scopus", "@href"=>"https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84873259375&origin=inward"}, {"@_fa"=>"true", "@ref"=>"scopus-citedby", "@href"=>"https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=84873259375&origin=inward"}], "prism:url"=>"https://api.elsevier.com/content/abstract/scopus_id/84873259375", "dc:identifier"=>"SCOPUS_ID:84873259375", "eid"=>"2-s2.0-84873259375", "dc:title"=>"Real-Time Parallel Processing of Grammatical Structure in the Fronto-Striatal System: A Recurrent Network Simulation Study Using Reservoir Computing", "dc:creator"=>"Hinaut X.", "prism:publicationName"=>"PLoS ONE", "prism:eIssn"=>"19326203", "prism:volume"=>"8", "prism:issueIdentifier"=>"2", "prism:pageRange"=>nil, "prism:coverDate"=>"2013-02-01", "prism:coverDisplayDate"=>"1 February 2013", "prism:doi"=>"10.1371/journal.pone.0052946", "citedby-count"=>"50", "affiliation"=>[{"@_fa"=>"true", "affilname"=>"Institut Cellule Souche et Cerveau", "affiliation-city"=>"Bron", "affiliation-country"=>"France"}, {"@_fa"=>"true", "affilname"=>"Université Claude Bernard Lyon 1", "affiliation-city"=>"Villeurbanne", "affiliation-country"=>"France"}], "pubmed-id"=>"23383296", "prism:aggregationType"=>"Journal", "subtype"=>"ar", "subtypeDescription"=>"Article", "article-number"=>"e52946", "source-id"=>"10600153309", "openaccess"=>"1", "openaccessFlag"=>true}

Facebook

  • {"url"=>"http%3A%2F%2Fjournals.plos.org%2Fplosone%2Farticle%3Fid%3D10.1371%252Fjournal.pone.0052946", "share_count"=>0, "like_count"=>0, "comment_count"=>0, "click_count"=>0, "total_count"=>0}

Journal Comments | Further Information

Twitter

Counter

  • {"month"=>"2", "year"=>"2013", "pdf_views"=>"200", "xml_views"=>"8", "html_views"=>"1660"}
  • {"month"=>"3", "year"=>"2013", "pdf_views"=>"41", "xml_views"=>"1", "html_views"=>"476"}
  • {"month"=>"4", "year"=>"2013", "pdf_views"=>"49", "xml_views"=>"3", "html_views"=>"219"}
  • {"month"=>"5", "year"=>"2013", "pdf_views"=>"46", "xml_views"=>"1", "html_views"=>"290"}
  • {"month"=>"6", "year"=>"2013", "pdf_views"=>"16", "xml_views"=>"0", "html_views"=>"126"}
  • {"month"=>"7", "year"=>"2013", "pdf_views"=>"21", "xml_views"=>"1", "html_views"=>"181"}
  • {"month"=>"8", "year"=>"2013", "pdf_views"=>"14", "xml_views"=>"0", "html_views"=>"115"}
  • {"month"=>"9", "year"=>"2013", "pdf_views"=>"23", "xml_views"=>"1", "html_views"=>"172"}
  • {"month"=>"10", "year"=>"2013", "pdf_views"=>"18", "xml_views"=>"2", "html_views"=>"103"}
  • {"month"=>"11", "year"=>"2013", "pdf_views"=>"10", "xml_views"=>"1", "html_views"=>"78"}
  • {"month"=>"12", "year"=>"2013", "pdf_views"=>"13", "xml_views"=>"0", "html_views"=>"83"}
  • {"month"=>"1", "year"=>"2014", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"88"}
  • {"month"=>"2", "year"=>"2014", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"94"}
  • {"month"=>"3", "year"=>"2014", "pdf_views"=>"14", "xml_views"=>"3", "html_views"=>"154"}
  • {"month"=>"4", "year"=>"2014", "pdf_views"=>"10", "xml_views"=>"1", "html_views"=>"136"}
  • {"month"=>"5", "year"=>"2014", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"127"}
  • {"month"=>"6", "year"=>"2014", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"108"}
  • {"month"=>"7", "year"=>"2014", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"107"}
  • {"month"=>"8", "year"=>"2014", "pdf_views"=>"6", "xml_views"=>"2", "html_views"=>"67"}
  • {"month"=>"9", "year"=>"2014", "pdf_views"=>"2", "xml_views"=>"1", "html_views"=>"78"}
  • {"month"=>"10", "year"=>"2014", "pdf_views"=>"12", "xml_views"=>"1", "html_views"=>"159"}
  • {"month"=>"11", "year"=>"2014", "pdf_views"=>"12", "xml_views"=>"2", "html_views"=>"144"}
  • {"month"=>"12", "year"=>"2014", "pdf_views"=>"8", "xml_views"=>"1", "html_views"=>"83"}
  • {"month"=>"1", "year"=>"2015", "pdf_views"=>"10", "xml_views"=>"1", "html_views"=>"116"}
  • {"month"=>"2", "year"=>"2015", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"167"}
  • {"month"=>"3", "year"=>"2015", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"161"}
  • {"month"=>"4", "year"=>"2015", "pdf_views"=>"10", "xml_views"=>"1", "html_views"=>"174"}
  • {"month"=>"5", "year"=>"2015", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"170"}
  • {"month"=>"6", "year"=>"2015", "pdf_views"=>"19", "xml_views"=>"0", "html_views"=>"92"}
  • {"month"=>"7", "year"=>"2015", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"105"}
  • {"month"=>"8", "year"=>"2015", "pdf_views"=>"3", "xml_views"=>"2", "html_views"=>"88"}
  • {"month"=>"9", "year"=>"2015", "pdf_views"=>"28", "xml_views"=>"0", "html_views"=>"130"}
  • {"month"=>"10", "year"=>"2015", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"93"}
  • {"month"=>"11", "year"=>"2015", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"106"}
  • {"month"=>"12", "year"=>"2015", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"101"}
  • {"month"=>"1", "year"=>"2016", "pdf_views"=>"21", "xml_views"=>"0", "html_views"=>"133"}
  • {"month"=>"2", "year"=>"2016", "pdf_views"=>"2", "xml_views"=>"0", "html_views"=>"179"}
  • {"month"=>"3", "year"=>"2016", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"134"}
  • {"month"=>"4", "year"=>"2016", "pdf_views"=>"15", "xml_views"=>"0", "html_views"=>"117"}
  • {"month"=>"5", "year"=>"2016", "pdf_views"=>"21", "xml_views"=>"0", "html_views"=>"67"}
  • {"month"=>"6", "year"=>"2016", "pdf_views"=>"24", "xml_views"=>"0", "html_views"=>"87"}
  • {"month"=>"7", "year"=>"2016", "pdf_views"=>"6", "xml_views"=>"0", "html_views"=>"67"}
  • {"month"=>"8", "year"=>"2016", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"88"}
  • {"month"=>"9", "year"=>"2016", "pdf_views"=>"12", "xml_views"=>"0", "html_views"=>"82"}
  • {"month"=>"10", "year"=>"2016", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"133"}
  • {"month"=>"11", "year"=>"2016", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"62"}
  • {"month"=>"12", "year"=>"2016", "pdf_views"=>"13", "xml_views"=>"1", "html_views"=>"52"}
  • {"month"=>"1", "year"=>"2017", "pdf_views"=>"9", "xml_views"=>"1", "html_views"=>"51"}
  • {"month"=>"2", "year"=>"2017", "pdf_views"=>"16", "xml_views"=>"0", "html_views"=>"77"}
  • {"month"=>"3", "year"=>"2017", "pdf_views"=>"18", "xml_views"=>"0", "html_views"=>"114"}
  • {"month"=>"4", "year"=>"2017", "pdf_views"=>"14", "xml_views"=>"0", "html_views"=>"83"}
  • {"month"=>"5", "year"=>"2017", "pdf_views"=>"23", "xml_views"=>"1", "html_views"=>"71"}
  • {"month"=>"6", "year"=>"2017", "pdf_views"=>"15", "xml_views"=>"1", "html_views"=>"88"}
  • {"month"=>"7", "year"=>"2017", "pdf_views"=>"7", "xml_views"=>"2", "html_views"=>"53"}
  • {"month"=>"8", "year"=>"2017", "pdf_views"=>"5", "xml_views"=>"1", "html_views"=>"26"}
  • {"month"=>"9", "year"=>"2017", "pdf_views"=>"7", "xml_views"=>"1", "html_views"=>"47"}
  • {"month"=>"10", "year"=>"2017", "pdf_views"=>"14", "xml_views"=>"3", "html_views"=>"54"}
  • {"month"=>"11", "year"=>"2017", "pdf_views"=>"10", "xml_views"=>"0", "html_views"=>"92"}
  • {"month"=>"12", "year"=>"2017", "pdf_views"=>"15", "xml_views"=>"1", "html_views"=>"58"}
  • {"month"=>"1", "year"=>"2018", "pdf_views"=>"6", "xml_views"=>"0", "html_views"=>"39"}
  • {"month"=>"2", "year"=>"2018", "pdf_views"=>"10", "xml_views"=>"0", "html_views"=>"16"}
  • {"month"=>"3", "year"=>"2018", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"24"}
  • {"month"=>"4", "year"=>"2018", "pdf_views"=>"19", "xml_views"=>"1", "html_views"=>"28"}
  • {"month"=>"5", "year"=>"2018", "pdf_views"=>"8", "xml_views"=>"1", "html_views"=>"21"}
  • {"month"=>"6", "year"=>"2018", "pdf_views"=>"11", "xml_views"=>"1", "html_views"=>"25"}
  • {"month"=>"7", "year"=>"2018", "pdf_views"=>"2", "xml_views"=>"7", "html_views"=>"19"}
  • {"month"=>"8", "year"=>"2018", "pdf_views"=>"8", "xml_views"=>"1", "html_views"=>"17"}
  • {"month"=>"9", "year"=>"2018", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"12"}
  • {"month"=>"10", "year"=>"2018", "pdf_views"=>"16", "xml_views"=>"1", "html_views"=>"28"}
  • {"month"=>"11", "year"=>"2018", "pdf_views"=>"13", "xml_views"=>"0", "html_views"=>"29"}
  • {"month"=>"12", "year"=>"2018", "pdf_views"=>"13", "xml_views"=>"1", "html_views"=>"25"}
  • {"month"=>"1", "year"=>"2019", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"29"}
  • {"month"=>"2", "year"=>"2019", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"28"}
  • {"month"=>"3", "year"=>"2019", "pdf_views"=>"27", "xml_views"=>"0", "html_views"=>"42"}
  • {"month"=>"4", "year"=>"2019", "pdf_views"=>"22", "xml_views"=>"0", "html_views"=>"25"}
  • {"month"=>"5", "year"=>"2019", "pdf_views"=>"13", "xml_views"=>"0", "html_views"=>"19"}
  • {"month"=>"6", "year"=>"2019", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"11"}

Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/496195"], "description"=>"<p>Effects of spectral radius and time constant (τ) on performance. The error was averaged over 10 different reservoir instances. Colored cells indicate the mean error for those parameter setting. Note that in all four studies, there is a region along the diagonal where robust performance is observed. Number of units in the reservoir: N = 1000.</p>", "links"=>[], "tags"=>["cross-validation", "462", "grammatical"], "article_id"=>166720, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Neuroscience", "Physiology"], "users"=>["Xavier Hinaut", "Peter Ford Dominey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052946.g008", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameter_exploration_sensitivity_analysis_for_cross_validation_performance_with_462_grammatical_constructions_/166720", "title"=>"Parameter exploration (sensitivity analysis) for cross-validation performance with 462 grammatical constructions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-01 01:52:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/495971"], "description"=>"<p>Same sentences as illustrated in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052946#pone-0052946-g002\" target=\"_blank\">Figure 2</a>. In all cases, the model correctly determines the appropriate thematic roles for Noun 2 (and Noun 1 – not illustrated). Simulation conditions, activation time, AT  = 20, and number of reservoir units, N = 100.</p>", "links"=>[], "tags"=>["grammatical", "constructions"], "article_id"=>166496, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Neuroscience", "Physiology"], "users"=>["Xavier Hinaut", "Peter Ford Dominey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052946.g006", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Generalization_to_new_grammatical_constructions_that_were_not_present_in_the_training_set_/166496", "title"=>"Generalization to new grammatical constructions that were not present in the training set.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-01 01:48:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/496397"], "description"=>"<p>Meaning and Sentence Error for generalization to new construction (y-axis) as a function of the percentage of the corpus used for training. Generalization performance increases with percentage of corpus exposure in training. With exposure to only 12.5% of the corpus, the system generalizes with meaning error <4% and sentence error <22% for sentence final learning. Exposure to 50% of the corpus allowed generalization with 1.78% meaning error and 15.01% sentence error. Note that 12% of the sentences are ambiguous and will compete, rendering the real possible minimal sentence error ∼12%. Each point is an average over 5 instances, with means and standard deviations shown.</p>", "links"=>[], "tags"=>["90k"], "article_id"=>166921, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Neuroscience", "Physiology"], "users"=>["Xavier Hinaut", "Peter Ford Dominey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052946.g011", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effects_of_progressive_exposure_to_90K_corpus_/166921", "title"=>"Effects of progressive exposure to 90K corpus.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-01 01:55:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/496456"], "description"=>"<p>Errors are given in percent. Different learning conditions: SFL – sentence final learning (SR = 1, τ = 6), CL – continuous learning (SR = 1, τ = 6), CL* optimized continuous learning (SR = 6, τ = 55). <i>m err</i> and <i>s err</i> are for <i>meaning</i> and <i>sentence</i> error respectively. std: standard deviation. Simulations were done with N = 1000 internal units.</p>", "links"=>[], "tags"=>["deviation", "conditions"], "article_id"=>166980, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Neuroscience", "Physiology"], "users"=>["Xavier Hinaut", "Peter Ford Dominey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052946.t001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_and_standard_deviation_error_in_different_learning_conditions_for_train_and_test_sets_/166980", "title"=>"Mean and standard deviation error in different learning conditions for train and test sets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-01 01:56:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/495482"], "description"=>"<p>A. Grammatical construction processing in the reservoir framework. Semantic and grammatical words (i.e. open and closed class words, respectively) are separated on input. Semantic words (SW) are stored in a memory stack. Grammatical words and a single input for all SWs are inputs to the reservoir (analogous to prefrontal cortex area BA47). During training, input sentences are presented word-by-word, and readout units (corresponding to striatum) are forced to the corresponding coded meaning (i.e. SW1-Object, SW2-Predicate, SW3-Agent). In testing, readout units code the predicted role(s) of each semantic word, forming the coded meaning. The meaning (i.e. hit(Mary, John, _)) can be reconstructed from the coded meaning, as SWs in memory stack are reassigned to the thematic roles (predicate, agent, object, recipient) identified in the read-outs. B. Active and passive grammatical constructions (i.e. mapping from sentence form to meaning), and their shared meaning. Coded meaning (indicated by the arrows) corresponds to specific mapping from open class words to meaning, which defines the grammatical construction.</p>", "links"=>[], "tags"=>["correspondence", "cortico-striatal", "reservoir", "implementations"], "article_id"=>166007, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Neuroscience", "Physiology"], "users"=>["Xavier Hinaut", "Peter Ford Dominey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052946.g001", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Thematic_role_assignment_and_correspondence_between_cortico_striatal_and_reservoir_implementations_of_sentence_processing_/166007", "title"=>"Thematic role assignment and correspondence between cortico-striatal and reservoir implementations of sentence processing.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-01 01:40:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/496358"], "description"=>"<p>Active and passive surface forms “John gave the ball to Mary” and “The ball was given to Mary by John” have different surface forms. They also have different coded meanings, e.g. the first semantic word (SW1) is the agent in the active, and the object in the passive. But these two coded meanings correspond to the same meaning <i>Gave(John, ball, Mary)</i> in the format <i>Predicate(Agent, Object, Recipient)</i>. Ambiguous sentences have the same surface form, but correspond to two different coded meanings and two different meanings. Finally, redundant sentences have different surface forms, but the same coded meaning and meaning.</p>", "links"=>[], "tags"=>["forms"], "article_id"=>166882, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Neuroscience", "Physiology"], "users"=>["Xavier Hinaut", "Peter Ford Dominey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052946.g010", "stats"=>{"downloads"=>1, "page_views"=>40, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Different_forms_of_meaning_relations_/166882", "title"=>"Different forms of meaning relations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-01 01:54:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/495855"], "description"=>"<p>Green trace: Calculated sum of change (instantaneous temporal derivative) in neural activity over two successive time steps for Subject- and Object-relative sentences in A and B respectively. Corresponds to neural “effort” serving as an index related to human ERPs. This activity change can be compared with the human P600 response. For A and B sentence onset generates a significant change in activity. Arrival of relativizer “that” generates another significant change (indicating that this is a relatively low probability event in the corpus). The crucial comparison is for the next word (“V” and “the” respectively in A and B, marked with arrow). Arrival of “V” in A indicates with the subject-relative structure that is of higher probability in the corpus, and leads to small activity change. Arrival of “the” in B indicates the low frequency (only one in the corpus) object-relative, and generates a greater activity change. Blue trace – Sum diff. – simple sum of activity differences between two time steps. Green trace – Abs. sum diff. – sum of absolute values of activity differences. Red trace – Abs. max. diff. – absolute value of the maximum activity differences between two time steps. Blue and red traces are provided as additional information. Simulation conditions, activation time, AT  = 20, and number of reservoir units, N = 300.</p>", "links"=>[], "tags"=>["instantaneous", "changes"], "article_id"=>166383, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Neuroscience", "Physiology"], "users"=>["Xavier Hinaut", "Peter Ford Dominey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052946.g005", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Global_instantaneous_changes_in_output_activity_/166383", "title"=>"Global instantaneous changes in output activity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-01 01:46:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/496085"], "description"=>"<p>Four example discourses. A–C and B–D use the same first sentence form respectively, and A–B and C–D use the same second sentence form, respectively. This yields four distinct coded meaning patterns. For each, in the second sentence, the anaphoric reference of “he” and “it” must be resolved, such that “he” is associated with the proper noun, and “it” with the common noun (i.e. the one followed by “the”) from the first sentence. A. Noun 1 is the Agent of Action 1 and Action 2. B. Noun 1 is the Object of Action 1 and 2 C. Agent 1 Object 2 D. Object 1 Agent 2. Note that when comparing “then he hit it” in A and B, the coded meaning is different, dependant on the preceding sentence in the discourse. Similar for “then it hit him” in C and D. Simulation conditions, activation time, AT  = 20, and number of reservoir units, N = 300.</p>", "links"=>[], "tags"=>["physiology", "Computational biology", "neuroscience", "computer science"], "article_id"=>166608, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Neuroscience", "Physiology"], "users"=>["Xavier Hinaut", "Peter Ford Dominey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052946.g007", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Two_sentence_discourse_processing_/166608", "title"=>"Two-sentence discourse processing.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-01 01:50:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/495559"], "description"=>"<p>Neurons coding different thematic roles indicated by colored traces (see inserted legend). For all four sentences (see period before arrow (a)), the model initially predicts that Noun 2 is the Object of Action 1 (green trace). In B and C this remains true, but Noun 2 is also the Agent and Object of Action 2 in B and C respectively. At point (b), arrival of “to” confirms the correct prediction of N2-O1 (green trace) in A, and the arrival of “that” induces a change in activity in B and C, with increased prediction of both Agent and Object roles for V2, respectively. Note that this is resolved at the arrival of the “V” and “was” in B and C respectively (arrow (c)). In D the arrival of “was” provokes a new analysis with Noun 2 as the Agent of Action 1. Embedded legend: N2-A1 – Noun 2 is the agent of Action 1. A – Agent, O – Object, R – Recipient. Simulation conditions, activation time, AT  = 20, and number of reservoir units, N = 300.</p>", "links"=>[], "tags"=>["readout", "neurons", "noun"], "article_id"=>166091, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Neuroscience", "Physiology"], "users"=>["Xavier Hinaut", "Peter Ford Dominey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052946.g002", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Output_of_the_8220_striatal_8221_readout_neurons_for_Noun_2_for_four_example_sentences_/166091", "title"=>"Output of the “striatal” readout neurons for Noun 2 for four example sentences.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-01 01:41:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/496276"], "description"=>"<p>Meaning and Sentence Error (y-axis) as a function of the number of neurons (x-axis). Logarithmic scale. Each point is an average over 5 instances, with means and standard deviations shown. Cross-validation error reduces with reservoir size. This indicates that within the range of reservoir sizes studied, overfitting does not increase with reservoir size. Arrow marks reservoir size of N = 1059, for comparison with N = 1000 results in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052946#pone-0052946-t001\" target=\"_blank\">Table 1</a> and Experiment 5. CL – continuous learning (SR = 1, τ = 6), CL* optimized continuous learning (SR = 6, τ = 55), SFL – sentence final learning (SR = 1, τ = 6).</p>", "links"=>[], "tags"=>["reservoir", "cross-validation", "462"], "article_id"=>166794, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Neuroscience", "Physiology"], "users"=>["Xavier Hinaut", "Peter Ford Dominey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052946.g009", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effects_of_reservoir_size_on_cross_validation_with_the_462_corpus_/166794", "title"=>"Effects of reservoir size on cross-validation with the 462 corpus.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-01 01:53:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/495676"], "description"=>"<p>Both sentences begin with “The N that...”. A. Subject-relative (first noun is subject of principal and relative clauses) “The N that <b>V</b> the N was V by the N”. Arrival of the “V” following “that” produces a shift in activity coding for N1-A1, i.e. Noun 1 is the Agent of Verb 1. At the arrival of the second “V”, we observe the increase in activity in N1-A2, coding N1 as the Agent of Verb 2. B. Object-relative (first noun is subject of principal and object of relative clause) “The N that the N V V the N.” Arrival of the second “the” generates a shift in the coded meaning with N1 assigned the role of Object of Action 1 (N1-O1), and subsequently Agent of Action 2 (N1-A2). It is of interest to compare the responses in to the second “V” in sentence 20 (Fig. 3A) to the responses at the same point to “was” in sentence 22 “The N that V the N was V by the N” in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052946#pone.0052946.s005\" target=\"_blank\">Text S2</a>, where the two neurons coding Agent and Object of Verb 2 shift in the opposite sense. Simulation conditions, activation time, AT  = 20, and number of reservoir units, N = 300.</p>", "links"=>[], "tags"=>["physiology", "Computational biology", "neuroscience", "computer science"], "article_id"=>166199, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Neuroscience", "Physiology"], "users"=>["Xavier Hinaut", "Peter Ford Dominey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052946.g003", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Processing_relative_phrases_/166199", "title"=>"Processing relative phrases.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-01 01:43:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/495755"], "description"=>"<p>A. Subject-relative. For the word “V” following “that”, there is relatively small change in the readout neurons, indicating that the predictions of the model were essentially confirmed. B. Object-relative. For the “the” following “that”, there is a significant shift in activity, corresponding to a re-assignment of the most probable coded meaning. Simulation conditions, activation time, AT  = 20, and number of reservoir units, N = 300.</p>", "links"=>[], "tags"=>["sentences", "modified", "corpus", "subject-relatives"], "article_id"=>166277, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Neuroscience", "Physiology"], "users"=>["Xavier Hinaut", "Peter Ford Dominey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052946.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relative_sentences_with_modified_corpus_distribution_such_that_subject_relatives_are_more_frequent_that_object_relatives_/166277", "title"=>"Relative sentences with modified corpus distribution such that subject-relatives are more frequent that object-relatives.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-01 01:44:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/481743", "https://ndownloader.figshare.com/files/481745", "https://ndownloader.figshare.com/files/481746", "https://ndownloader.figshare.com/files/481748", "https://ndownloader.figshare.com/files/481750", "https://ndownloader.figshare.com/files/481758", "https://ndownloader.figshare.com/files/481760", "https://ndownloader.figshare.com/files/481765"], "description"=>"<div><p>Sentence processing takes place in real-time. Previous words in the sentence can influence the processing of the current word in the timescale of hundreds of milliseconds. Recent neurophysiological studies in humans suggest that the fronto-striatal system (frontal cortex, and striatum – the major input locus of the basal ganglia) plays a crucial role in this process. The current research provides a possible explanation of how certain aspects of this real-time processing can occur, based on the dynamics of recurrent cortical networks, and plasticity in the cortico-striatal system. We simulate prefrontal area BA47 as a recurrent network that receives on-line input about word categories during sentence processing, with plastic connections between cortex and striatum. We exploit the homology between the cortico-striatal system and reservoir computing, where recurrent frontal cortical networks are the reservoir, and plastic cortico-striatal synapses are the readout. The system is trained on sentence-meaning pairs, where meaning is coded as activation in the striatum corresponding to the roles that different nouns and verbs play in the sentences. The model learns an extended set of grammatical constructions, and demonstrates the ability to generalize to novel constructions. It demonstrates how early in the sentence, a parallel set of predictions are made concerning the meaning, which are then confirmed or updated as the processing of the input sentence proceeds. It demonstrates how on-line responses to words are influenced by previous words in the sentence, and by previous sentences in the discourse, providing new insight into the neurophysiology of the P600 ERP scalp response to grammatical complexity. This demonstrates that a recurrent neural network can decode grammatical structure from sentences in real-time in order to generate a predictive representation of the meaning of the sentences. This can provide insight into the underlying mechanisms of human cortico-striatal function in sentence processing.</p> </div>", "links"=>[], "tags"=>["Real-time", "parallel", "grammatical", "fronto-striatal", "recurrent", "simulation", "reservoir", "computing"], "article_id"=>155620, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Neuroscience", "Physiology"], "users"=>["Xavier Hinaut", "Peter Ford Dominey"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0052946.s001", "https://dx.doi.org/10.1371/journal.pone.0052946.s002", "https://dx.doi.org/10.1371/journal.pone.0052946.s003", "https://dx.doi.org/10.1371/journal.pone.0052946.s004", "https://dx.doi.org/10.1371/journal.pone.0052946.s005", "https://dx.doi.org/10.1371/journal.pone.0052946.s006", "https://dx.doi.org/10.1371/journal.pone.0052946.s007", "https://dx.doi.org/10.1371/journal.pone.0052946.s008"], "stats"=>{"downloads"=>5, "page_views"=>43, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Real_Time_Parallel_Processing_of_Grammatical_Structure_in_the_Fronto_Striatal_System_A_Recurrent_Network_Simulation_Study_Using_Reservoir_Computing__/155620", "title"=>"Real-Time Parallel Processing of Grammatical Structure in the Fronto-Striatal System: A Recurrent Network Simulation Study Using Reservoir Computing", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-02-01 01:33:40"}

PMC Usage Stats | Further Information

  • {"unique-ip"=>"10", "full-text"=>"8", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"14", "cited-by"=>"0", "year"=>"2013", "month"=>"3"}
  • {"unique-ip"=>"25", "full-text"=>"18", "pdf"=>"10", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"6", "supp-data"=>"28", "cited-by"=>"0", "year"=>"2013", "month"=>"2"}
  • {"unique-ip"=>"9", "full-text"=>"9", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"21", "cited-by"=>"0", "year"=>"2013", "month"=>"4"}
  • {"unique-ip"=>"30", "full-text"=>"33", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"5"}
  • {"unique-ip"=>"8", "full-text"=>"20", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"14", "cited-by"=>"0", "year"=>"2013", "month"=>"6"}
  • {"unique-ip"=>"11", "full-text"=>"18", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"7", "cited-by"=>"0", "year"=>"2013", "month"=>"7"}
  • {"unique-ip"=>"2", "full-text"=>"1", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"8"}
  • {"unique-ip"=>"5", "full-text"=>"7", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"7", "cited-by"=>"0", "year"=>"2013", "month"=>"9"}
  • {"unique-ip"=>"8", "full-text"=>"9", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2013", "month"=>"10"}
  • {"unique-ip"=>"8", "full-text"=>"9", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"11"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"2", "abstract"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2013", "month"=>"12"}
  • {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"2", "abstract"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"1"}
  • {"unique-ip"=>"10", "full-text"=>"12", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2014", "month"=>"2"}
  • {"unique-ip"=>"16", "full-text"=>"15", "pdf"=>"2", "abstract"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"5", "cited-by"=>"0", "year"=>"2014", "month"=>"3"}
  • {"unique-ip"=>"8", "full-text"=>"5", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2014", "month"=>"5"}
  • {"unique-ip"=>"8", "full-text"=>"6", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2014", "month"=>"6"}
  • {"unique-ip"=>"8", "full-text"=>"4", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"5", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"4"}
  • {"unique-ip"=>"11", "full-text"=>"15", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"4"}
  • {"unique-ip"=>"18", "full-text"=>"17", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2015", "month"=>"5"}
  • {"unique-ip"=>"17", "full-text"=>"18", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"6"}
  • {"unique-ip"=>"46", "full-text"=>"51", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"5", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"7"}
  • {"unique-ip"=>"14", "full-text"=>"18", "pdf"=>"5", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"3"}
  • {"unique-ip"=>"15", "full-text"=>"15", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"2"}
  • {"unique-ip"=>"30", "full-text"=>"30", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"8"}
  • {"unique-ip"=>"23", "full-text"=>"28", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"9"}
  • {"unique-ip"=>"6", "full-text"=>"4", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"10"}
  • {"unique-ip"=>"5", "full-text"=>"2", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2014", "month"=>"7"}
  • {"unique-ip"=>"10", "full-text"=>"15", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2014", "month"=>"8"}
  • {"unique-ip"=>"24", "full-text"=>"21", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"6", "cited-by"=>"0", "year"=>"2014", "month"=>"9"}
  • {"unique-ip"=>"33", "full-text"=>"47", "pdf"=>"7", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2014", "month"=>"10"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"2"}
  • {"unique-ip"=>"25", "full-text"=>"49", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2014", "month"=>"11"}
  • {"unique-ip"=>"8", "full-text"=>"14", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"12"}
  • {"unique-ip"=>"10", "full-text"=>"11", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2015", "month"=>"1"}
  • {"unique-ip"=>"5", "full-text"=>"0", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2015", "month"=>"11"}
  • {"unique-ip"=>"11", "full-text"=>"11", "pdf"=>"8", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"12"}
  • {"unique-ip"=>"10", "full-text"=>"11", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"1"}
  • {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"3"}
  • {"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"=>"4"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"5"}
  • {"unique-ip"=>"6", "full-text"=>"8", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"6"}
  • {"unique-ip"=>"11", "full-text"=>"10", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"7"}
  • {"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"=>"8"}
  • {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"9"}
  • {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"10"}
  • {"unique-ip"=>"17", "full-text"=>"16", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"11"}
  • {"unique-ip"=>"8", "full-text"=>"9", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"12"}
  • {"unique-ip"=>"4", "full-text"=>"3", "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"=>"3", "full-text"=>"2", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2017", "month"=>"2"}
  • {"unique-ip"=>"11", "full-text"=>"12", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"3"}
  • {"unique-ip"=>"10", "full-text"=>"9", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"4"}
  • {"unique-ip"=>"6", "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"=>"5"}
  • {"unique-ip"=>"5", "full-text"=>"6", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"6"}
  • {"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"=>"7"}
  • {"unique-ip"=>"7", "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"=>"8"}
  • {"unique-ip"=>"11", "full-text"=>"8", "pdf"=>"5", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2017", "month"=>"9"}
  • {"unique-ip"=>"10", "full-text"=>"5", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"12", "cited-by"=>"0", "year"=>"2017", "month"=>"10"}
  • {"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"=>"2017", "month"=>"11"}
  • {"unique-ip"=>"5", "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"=>"12"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"1"}
  • {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"6", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
  • {"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"=>"9"}
  • {"unique-ip"=>"8", "full-text"=>"8", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
  • {"unique-ip"=>"10", "full-text"=>"12", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"2", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"18", "full-text"=>"12", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"8", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"8", "full-text"=>"8", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"9", "full-text"=>"6", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"11", "full-text"=>"7", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"10", "full-text"=>"13", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"10", "full-text"=>"13", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"13", "full-text"=>"11", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"8", "full-text"=>"8", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Biology and life sciences/Cell biology", "average_usage"=>[272, 472, 600, 713, 815, 911, 1004, 1094, 1185, 1273, 1358, 1441]}, {"subject_area"=>"/Biology and life sciences/Neuroscience", "average_usage"=>[261, 444, 554, 655, 748, 834, 923, 1004, 1089, 1170, 1244, 1315, 1380]}, {"subject_area"=>"/Biology and life sciences/Psychology", "average_usage"=>[284, 458, 579, 688, 782, 879, 957, 1048, 1126, 1204, 1280, 1357, 1415]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[297, 488, 616, 724, 828, 939, 1038, 1127, 1223, 1311, 1393, 1479, 1556]}, {"subject_area"=>"/Computer and information sciences/Neural networks", "average_usage"=>[284, 481, 590, 705, 782, 880, 969, 1051, 1141, 1227, 1337, 1399, 1463]}, {"subject_area"=>"/Medicine and health sciences/Anatomy", "average_usage"=>[248, 441, 564, 668, 769, 859, 948, 1034, 1117, 1202, 1290, 1368, 1437]}, {"subject_area"=>"/Social sciences", "average_usage"=>[289, 475, 593, 703, 805, 902, 990, 1078, 1158, 1250, 1336, 1417, 1482]}]}
Loading … Spinner
There are currently no alerts