Encoding of Temporal Information by Timing, Rate, and Place in Cat Auditory Cortex
Publication Date
July 19, 2010
Journal
PLOS ONE
Authors
Kazuo Imaizumi, Nicholas J. Priebe, Tatyana O. Sharpee, Steven W. Cheung, et al
Volume
5
Issue
7
Pages
e11531
DOI
https://dx.plos.org/10.1371/journal.pone.0011531
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0011531
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/20657832
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2906504
Europe PMC
http://europepmc.org/abstract/MED/20657832
Web of Science
000280065600002
Scopus
77955391793
Mendeley
http://www.mendeley.com/research/encoding-temporal-information-timing-rate-place-cat-auditory-cortex
Events
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Mendeley | Further Information

{"title"=>"Encoding of temporal information by timing, rate, and place in cat auditory cortex", "type"=>"journal", "authors"=>[{"first_name"=>"Kazuo", "last_name"=>"Imaizumi", "scopus_author_id"=>"7202367807"}, {"first_name"=>"Nicholas J.", "last_name"=>"Priebe", "scopus_author_id"=>"6604070539"}, {"first_name"=>"Tatyana O.", "last_name"=>"Sharpee", "scopus_author_id"=>"6507966621"}, {"first_name"=>"Steven W.", "last_name"=>"Cheung", "scopus_author_id"=>"7202473469"}, {"first_name"=>"Christoph E.", "last_name"=>"Schreiner", "scopus_author_id"=>"7006315409"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-77955391793", "sgr"=>"77955391793", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0011531", "pmid"=>"20657832", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pui"=>"359319209"}, "id"=>"dd47d40a-c685-358e-89b2-b4885dd48574", "abstract"=>"A central goal in auditory neuroscience is to understand the neural coding of species-specific communication and human speech sounds. Low-rate repetitive sounds are elemental features of communication sounds, and core auditory cortical regions have been implicated in processing these information-bearing elements. Repetitive sounds could be encoded by at least three neural response properties: 1) the event-locked spike-timing precision, 2) the mean firing rate, and 3) the interspike interval (ISI). To determine how well these response aspects capture information about the repetition rate stimulus, we measured local group responses of cortical neurons in cat anterior auditory field (AAF) to click trains and calculated their mutual information based on these different codes. ISIs of the multiunit responses carried substantially higher information about low repetition rates than either spike-timing precision or firing rate. Combining firing rate and ISI codes was synergistic and captured modestly more repetition information. Spatial distribution analyses showed distinct local clustering properties for each encoding scheme for repetition information indicative of a place code. Diversity in local processing emphasis and distribution of different repetition rate codes across AAF may give rise to concurrent feed-forward processing streams that contribute differently to higher-order sound analysis.", "link"=>"http://www.mendeley.com/research/encoding-temporal-information-timing-rate-place-cat-auditory-cortex", "reader_count"=>87, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>7, "Researcher"=>27, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>23, "Other"=>5, "Student > Master"=>8, "Student > Bachelor"=>4, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>2, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>7, "Researcher"=>27, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>23, "Other"=>5, "Student > Master"=>8, "Student > Bachelor"=>4, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>2, "Professor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>5, "Unspecified"=>5, "Mathematics"=>1, "Agricultural and Biological Sciences"=>32, "Medicine and Dentistry"=>6, "Neuroscience"=>16, "Veterinary Science and Veterinary Medicine"=>1, "Physics and Astronomy"=>1, "Psychology"=>11, "Social Sciences"=>1, "Computer Science"=>8}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>5}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Neuroscience"=>{"Neuroscience"=>16}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>11}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>32}, "Computer Science"=>{"Computer Science"=>8}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>5}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Turkey"=>1, "United States"=>5, "Japan"=>1, "United Kingdom"=>4, "Germany"=>6}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/840079"], "description"=>"<p>(A) Spatial cross-correlation values of raw VS values generated by repetition rates between 1 and 30Hz (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011531#pone.0011531.s004\" target=\"_blank\">Fig. S4A</a>) and plotted as a function of the logarithmic repetition rate difference for all three hemispheres. The solid line is a logarithmic fit. The gray area indicates non-significant correlation values (<i>n</i> = 16/45). (B) Cross-correlation values of raw FR generated by repetition rates between 1 and 30Hz (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011531#pone.0011531.s004\" target=\"_blank\">Fig. S4B</a>) and plotted as a function of the linear repetition rate difference. Non-significant correlations: <i>n</i> = 19/45. (C) Cross-correlation values of CV of ISI generated by repetition rates between 1 and 30Hz (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011531#pone.0011531.s004\" target=\"_blank\">Fig. S4C</a>) and plotted as a function of the logarithmic repetition rate difference. Non-significant correlations: <i>n</i> = 12/45.</p>", "links"=>[], "tags"=>["repetition"], "article_id"=>510539, "categories"=>["Neuroscience"], "users"=>["Kazuo Imaizumi", "Nicholas J. Priebe", "Tatyana O. Sharpee", "Steven W. Cheung", "Christoph E. Schreiner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011531.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Map_similarity_for_repetition_rate_differences_/510539", "title"=>"Map similarity for repetition rate differences.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-19 00:08:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/839655"], "description"=>"<p>(A) Mean (± standard error of the mean) of MI values for VS, FR, ISI (1 ms) and ISI (10 ms) for all three hemispheres. The global mean is indicated as a dashed line across the three hemispheres. MI for ISI (10 ms) was based on intervals equal or larger than 10 ms, whereas MI for ISI (1 ms) contained all intervals equal or larger than 1 ms. Paired <i>t</i> tests adjusted by the sequential Bonferroni correction for multiple comparisons (<i>p</i><0.001) were performed for the three global mean measures. A theoretical MI value for distinguishing six different repetition rate stimuli is 2.58 bits/stimulus ( = log<sub>2</sub>(6)). (B) Information captured for different combinations of a joint repetition rate code. Black/gray bars: additive combination of the two codes (Code<sub>(x)</sub> + Code<sub>(y)</sub>). The number of sites that resulted in valid joint information value was lower than the total number of sites for the individual information analysis. The summed information is based on recording sites that had a valid joint information. White bars: joint information values for two codes (Code<sub>(x)</sub> × Code<sub>(y)</sub>). Unpaired <i>t</i> tests for the comparison between additive and joint codes (<i>p</i><0.001).</p>", "links"=>[], "tags"=>["contained"], "article_id"=>510116, "categories"=>["Neuroscience"], "users"=>["Kazuo Imaizumi", "Nicholas J. Priebe", "Tatyana O. Sharpee", "Steven W. Cheung", "Christoph E. Schreiner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011531.g002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mutual_information_MI_contained_in_VS_FR_and_ISI_/510116", "title"=>"Mutual information (MI) contained in VS, FR, and ISI.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-19 00:01:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/840334"], "description"=>"<p>(A) Spatial distribution of the magnitudes of three temporal factors based on a principal component analysis of CV min, FR max, VS max as well as the three corresponding information measures (hemisphere 073L). White and black dots indicate polygons with statistically similar values than their direct neighbors (compared to random re-distribution of all neighbors, see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011531#s4\" target=\"_blank\">Materials and Methods</a>). Significant local clustering: *: <i>p</i><0.05; **: <i>p</i><0.01. D: dorsal, A: anterior, scale bars: 1 mm. For the tonotopic gradient, see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011531#pone.0011531.s003\" target=\"_blank\">Figure S3</a>. (B) Same as (A) for hemisphere 073R.</p>", "links"=>[], "tags"=>["distributions", "temporal"], "article_id"=>510791, "categories"=>["Neuroscience"], "users"=>["Kazuo Imaizumi", "Nicholas J. Priebe", "Tatyana O. Sharpee", "Steven W. Cheung", "Christoph E. Schreiner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011531.g007", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_distributions_of_temporal_response_factors_/510791", "title"=>"Spatial distributions of temporal response factors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-19 00:13:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/840544"], "description"=>"<p>Spatial autocorrelation analysis measured by Geary's C provides a global assessment of spatial organization versus random distribution. A stringent analysis of local similarity and parameter clustering was performed by determining average value differences of each polygon from its direct neighbors. Polygon similarity expresses the proportion of sites with significantly similar neighbors (range 0–1). Both global and local measures were validated by Monte-Carlo analysis. Bold: <i>p</i><0.05; italic: not significant.</p>", "links"=>[], "tags"=>["clustering", "cortical"], "article_id"=>511004, "categories"=>["Neuroscience"], "users"=>["Kazuo Imaizumi", "Nicholas J. Priebe", "Tatyana O. Sharpee", "Steven W. Cheung", "Christoph E. Schreiner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011531.t002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_clustering_statistics_of_cortical_maps_/511004", "title"=>"Spatial clustering statistics of cortical maps.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-07-19 00:16:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/839759"], "description"=>"<p>(A) Positive correlation between VS max and ISI info (<i>p</i><0.05). (B) Negative exponential correlation between ISI info and FR max (<i>p</i><0.0001). (C) Negative correlation between ISI info and CV min (<i>p</i><0.0001). (D) Weak positive correlation between VS info and VS max (<i>p</i><0.05).</p>", "links"=>[], "tags"=>["temporal"], "article_id"=>510219, "categories"=>["Neuroscience"], "users"=>["Kazuo Imaizumi", "Nicholas J. Priebe", "Tatyana O. Sharpee", "Steven W. Cheung", "Christoph E. Schreiner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011531.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_between_information_and_three_temporal_response_measures_/510219", "title"=>"Correlation between information and three temporal response measures.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-19 00:03:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/418378", "https://ndownloader.figshare.com/files/418388", "https://ndownloader.figshare.com/files/418398", "https://ndownloader.figshare.com/files/418407"], "description"=>"<div><p>A central goal in auditory neuroscience is to understand the neural coding of species-specific communication and human speech sounds. Low-rate repetitive sounds are elemental features of communication sounds, and core auditory cortical regions have been implicated in processing these information-bearing elements. Repetitive sounds could be encoded by at least three neural response properties: 1) the event-locked spike-timing precision, 2) the mean firing rate, and 3) the interspike interval (ISI). To determine how well these response aspects capture information about the repetition rate stimulus, we measured local group responses of cortical neurons in cat anterior auditory field (AAF) to click trains and calculated their mutual information based on these different codes. ISIs of the multiunit responses carried substantially higher information about low repetition rates than either spike-timing precision or firing rate. Combining firing rate and ISI codes was synergistic and captured modestly more repetition information. Spatial distribution analyses showed distinct local clustering properties for each encoding scheme for repetition information indicative of a place code. Diversity in local processing emphasis and distribution of different repetition rate codes across AAF may give rise to concurrent feed-forward processing streams that contribute differently to higher-order sound analysis.</p></div>", "links"=>[], "tags"=>["encoding", "temporal", "auditory", "cortex"], "article_id"=>142607, "categories"=>["Neuroscience"], "users"=>["Kazuo Imaizumi", "Nicholas J. Priebe", "Tatyana O. Sharpee", "Steven W. Cheung", "Christoph E. Schreiner"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0011531.s001", "https://dx.doi.org/10.1371/journal.pone.0011531.s002", "https://dx.doi.org/10.1371/journal.pone.0011531.s003", "https://dx.doi.org/10.1371/journal.pone.0011531.s004"], "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Encoding_of_Temporal_Information_by_Timing_Rate_and_Place_in_Cat_Auditory_Cortex/142607", "title"=>"Encoding of Temporal Information by Timing, Rate, and Place in Cat Auditory Cortex", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-07-19 00:43:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/840221"], "description"=>"<p>(A) Spatial distributions of CV min of ISI, VS max, and FR max for hemisphere 111L. Minimum or maximum value of the measures for any of the repetition rates is shown. White dots indicate polygons with statistically similar values as their direct neighbors (compared to random re-distribution of all neighbors, see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011531#s4\" target=\"_blank\">Materials and Methods</a>). Gray polygons indicate sites not available due to the four tested repetition rates, which also apply to (B, C). (B) Spatial distributions of mutual information values of ISI, VS, and FR based on repetition rate discrimination. (C) Spatial distribution of three temporal factors emerging from a principal component analysis of CV min, FR max, VS max, as well as the three corresponding information measures. Both white and black dots indicate polygons with statistically similar values as their direct neighbors.</p>", "links"=>[], "tags"=>["distributions", "temporal", "measures"], "article_id"=>510673, "categories"=>["Neuroscience"], "users"=>["Kazuo Imaizumi", "Nicholas J. Priebe", "Tatyana O. Sharpee", "Steven W. Cheung", "Christoph E. Schreiner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011531.g006", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_distributions_of_temporal_response_measures_and_mutual_information_/510673", "title"=>"Spatial distributions of temporal response measures and mutual information.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-19 00:11:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/840411"], "description"=>"<p>A scatter plot of the mean proportion of polygons with high magnitude similarity to directly neighboring polygons (local spatial organization) versus a mean spatial autocorrelation measure (Geary's C; global spatial organization) for all three hemispheres (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011531#pone-0011531-t002\" target=\"_blank\">Table 2</a>). Error bars indicate standard error of the mean. Light gray shading indicates statistically non-significant regions for either measure. Dark gray area corresponds to value range that is not statistically significant for either local or global measures. A linear regression line is shown (<i>r<sup>2</sup></i> = 0.92, <i>p</i><0.001). CF = characteristic frequency; Q40 = frequency tuning curve bandwidth (at 40 dB above threshold)/CF; Lat = minimum response latency at CF; F1(ISI) = strongest temporal factor comprising CV min, ISI info, and FR max; F2(VS) = second strongest temporal factor comprising VS max and VS info F5(FR) = third strongest temporal factor comprising FR info.</p>", "links"=>[], "tags"=>["spatial"], "article_id"=>510871, "categories"=>["Neuroscience"], "users"=>["Kazuo Imaizumi", "Nicholas J. Priebe", "Tatyana O. Sharpee", "Steven W. Cheung", "Christoph E. Schreiner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011531.g008", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Local_versus_global_spatial_organization_in_AAF_/510871", "title"=>"Local versus global spatial organization in AAF.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-19 00:14:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/840508"], "description"=>"<p>Three click train response parameters (VS max, maximum vector strength; FR max, maximum firing rate; CV min, minimum coefficient of variations for ISIs), three mutual information values (VS info; FR info, ISI info), and four basic receptive field parameters (CF, characteristic frequency; Q40, sharpness of tuning; Threshold, response threshold; Latency, minimum latency) were analyzed. Analysis was applied jointly to all sites of the three hemispheres. Total variance accounted for: 77.5%. Bold numbers: dominant factor loadings for each parameter. Five significant factors were identified (Bartlett's chi-square test; <i>p</i><0.05).</p>", "links"=>[], "tags"=>["neuroscience/cognitive neuroscience", "neuroscience/sensory systems", "neuroscience/theoretical neuroscience"], "article_id"=>510967, "categories"=>["Neuroscience"], "users"=>["Kazuo Imaizumi", "Nicholas J. Priebe", "Tatyana O. Sharpee", "Steven W. Cheung", "Christoph E. Schreiner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011531.t001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Principal_component_analysis_/510967", "title"=>"Principal component analysis.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-07-19 00:16:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/839556"], "description"=>"<p>(A) Poststimulus time histograms for 20 repetition rates for an AAF site. Response strength was normalized to the maximum response at 1 Hz. Maximum height of the FR ordinate: 15 spikes. Information values for 111L-S98: VS info: 0.62 bits/stimulus; FR info: 0.23 bits/stimulus; ISI (1 ms) info: 0.51 bits/stimulus; ISI (10 ms) info: 1.95 bits/stimulus. (B) Corresponding RRTFs for VS (magenta line) and FR (blue line). Data points are fit by a polynomial cubic spline. Filled circles are significant VS values (Rayleigh test, <i>p</i><0.001). Gray background illustrates the repetition rate range at the focus in this study. (C) ISI histogram for 6 repetition rates. Multiple ISI peaks correspond to integer multiples of stimulus periods. Information values for 111L-S93: VS info: 0.09 bits/stimulus; FR info: 0.11 bits/stimulus; ISI (1 ms) info: 1.20 bits/stimulus; ISI (10 ms): 2.28 bits/stimulus. (D) Population distribution of the coefficient of variation (CV) of ISIs for hemisphere 111L (<i>n</i> = 130). CV of ISI that estimates the variability of ISIs was computed by dividing the standard deviation of ISIs by the mean. Histograms of CV distributions, smoothed by a polynomial cubic spline, are illustrated for six different repetition-rates.</p>", "links"=>[], "tags"=>["functions"], "article_id"=>510010, "categories"=>["Neuroscience"], "users"=>["Kazuo Imaizumi", "Nicholas J. Priebe", "Tatyana O. Sharpee", "Steven W. Cheung", "Christoph E. Schreiner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011531.g001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Repetition_rate_transfer_functions_for_VS_and_FR_/510010", "title"=>"Repetition rate transfer functions for VS and FR.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-19 00:00:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/839972"], "description"=>"<p>(A) Tonotopic gradient smoothed by a weighted least-squares linear regression model is reconstructed on the cortical surface by Voronoi-Dirichlet tessellation. An approximate location of AAF is indicated by the suprasylvian sulcus (sss) and the anterior ectosylvian sulcus (aes; thick black lines). Hemisphere 111L; D: dorsal, A: anterior, scale bars: 1 mm. (B) Spatial representation of VS as a function of different repetition rate. Repetition rates are shown on the top. White polygons indicate sites not tested for the corresponding repetition rates, which also apply to (C, D). Raw VS values of the Voronoi-Dirichlet tessellation maps were smoothed by a weighted least-squares linear regression model. (C) Spatial distribution of FR as a function of repetition rate. FR magnitude was normalized to the peak rate for the corresponding repetition rate. Normalized FR magnitudes were smoothed by a weighted least-squares linear regression model. High activity sites with FR>0.75 in the smoothed maps were categorized and shown in the bottom panel as red polygons. Sites with FR<0.75 are illustrated by gray polygons. (D) Spatial distribution of CV of ISI as a function of repetition rate. Spatially smoothed maps are shown. D: dorsal, A: anterior, scale bars: 1 mm. The scales also apply to (B, C).</p>", "links"=>[], "tags"=>["repetition"], "article_id"=>510428, "categories"=>["Neuroscience"], "users"=>["Kazuo Imaizumi", "Nicholas J. Priebe", "Tatyana O. Sharpee", "Steven W. Cheung", "Christoph E. Schreiner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0011531.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_distribution_of_population_response_to_different_repetition_rates_/510428", "title"=>"Spatial distribution of population response to different repetition rates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-07-19 00:07:08"}

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  • {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}

Relative Metric

{"start_date"=>"2010-01-01T00:00:00Z", "end_date"=>"2010-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[288, 576, 733, 867, 984, 1087, 1182, 1267, 1346, 1424, 1501, 1577, 1646, 1711, 1778, 1841, 1908, 1970, 2034, 2102, 2162, 2227, 2296, 2359, 2422, 2482, 2550, 2610, 2679, 2747, 2820, 2887, 2955, 3009, 3067, 3130, 3200, 3257, 3322, 3379, 3443, 3507, 3571, 3632, 3683, 3753, 3822, 3877]}, {"subject_area"=>"/Biology and life sciences/Computational biology", "average_usage"=>[334, 662, 847, 1004, 1136, 1245, 1346, 1439, 1513, 1595, 1657, 1726, 1787, 1853, 1905, 1972, 2049, 2112, 2182, 2254, 2335, 2411, 2473, 2537, 2587, 2662, 2729, 2786, 2849, 2932, 2997, 3055, 3122, 3190, 3270, 3331, 3398, 3472, 3534, 3611, 3684, 3766, 3814, 3865, 3909, 3956, 4011, 4073, 4112]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[314, 590, 728, 851, 953, 1050, 1127, 1206, 1276, 1336, 1390, 1451, 1504, 1569, 1619, 1674, 1732, 1792, 1851, 1900, 1959, 2004, 2076, 2126, 2182, 2239, 2292, 2353, 2427, 2522, 2595, 2676, 2735, 2785, 2836, 2893, 2943, 2993, 3041, 3097, 3160, 3214, 3251, 3299, 3357, 3428, 3482, 3515, 3555]}, {"subject_area"=>"/Social sciences/Linguistics", "average_usage"=>[384, 577, 724, 824, 897, 1004, 1081, 1124, 1179, 1241, 1331, 1435, 1525, 1599, 1630, 1724, 1785, 1864, 1925, 1986, 2031, 2074, 2152, 2229, 2304, 2398, 2502, 2586, 2664, 2722, 2788, 2869, 2943, 3019, 3125, 3187, 3271, 3348, 3386, 3453, 3504, 3567, 3612, 3653, 3695, 3743, 3788, 3829, 3890]}]}
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