A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation
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{"title"=>"A high-throughput screening approach to discovering good forms of biologically inspired visual representation", "type"=>"journal", "authors"=>[{"first_name"=>"Nicolas", "last_name"=>"Pinto", "scopus_author_id"=>"35273071400"}, {"first_name"=>"David", "last_name"=>"Doukhan", "scopus_author_id"=>"57198883541"}, {"first_name"=>"James J.", "last_name"=>"DiCarlo", "scopus_author_id"=>"7006387907"}, {"first_name"=>"David D.", "last_name"=>"Cox", "scopus_author_id"=>"8923945700"}], "year"=>2009, "source"=>"PLoS Computational Biology", "identifiers"=>{"pmid"=>"19956750", "doi"=>"10.1371/journal.pcbi.1000579", "sgr"=>"73449129720", "isbn"=>"1553-7358 (Electronic)\\n1553-734X (Linking)", "scopus"=>"2-s2.0-73449129720", "issn"=>"1553734X", "pui"=>"358053159"}, "id"=>"080aab7a-8f2d-31ca-aef2-db388f13d239", "abstract"=>"While many models of biological object recognition share a common set of \"broad-stroke\" properties, the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model--e.g., the number of units per layer, the size of pooling kernels, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is typically large and the computational cost of evaluating one particular parameter set is high, the space of possible model instantiations goes largely unexplored. Thus, when a model fails to approach the abilities of biological visual systems, we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct \"parts\" have not been tuned correctly, assembled at sufficient scale, or provided with enough training. Here, we present a high-throughput approach to the exploration of such parameter sets, leveraging recent advances in stream processing hardware (high-end NVIDIA graphic cards and the PlayStation 3's IBM Cell Processor). In analogy to high-throughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. We show that this approach can yield significant, reproducible gains in performance across an array of basic object recognition tasks, consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature. As the scale of available computational power continues to expand, we argue that this approach has the potential to greatly accelerate progress in both artificial vision and our understanding of the computational underpinning of biological vision.", "link"=>"http://www.mendeley.com/research/highthroughput-screening-approach-discovering-good-forms-biologically-inspired-visual-representation", "reader_count"=>262, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>13, "Librarian"=>1, "Researcher"=>62, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>93, "Student > Postgraduate"=>8, "Student > Master"=>35, "Other"=>8, "Student > Bachelor"=>20, "Lecturer"=>1, "Professor"=>11}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>13, "Librarian"=>1, "Researcher"=>62, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>93, "Student > Postgraduate"=>8, "Student > Master"=>35, "Other"=>8, "Student > Bachelor"=>20, "Lecturer"=>1, "Professor"=>11}, "reader_count_by_subject_area"=>{"Unspecified"=>10, "Agricultural and Biological Sciences"=>46, "Chemistry"=>2, "Computer Science"=>110, "Economics, Econometrics and Finance"=>2, "Engineering"=>33, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>5, "Medicine and Dentistry"=>5, "Neuroscience"=>11, "Physics and Astronomy"=>13, "Psychology"=>21, "Social Sciences"=>3}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Social Sciences"=>{"Social Sciences"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>13}, "Psychology"=>{"Psychology"=>21}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>10}, "Engineering"=>{"Engineering"=>33}, "Chemistry"=>{"Chemistry"=>2}, "Neuroscience"=>{"Neuroscience"=>11}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>46}, "Computer Science"=>{"Computer Science"=>110}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}}, "reader_count_by_country"=>{"United States"=>27, "United Kingdom"=>8, "Spain"=>1, "Canada"=>1, "Iran"=>1, "Belgium"=>1, "Luxembourg"=>1, "Ireland"=>1, "Brazil"=>2, "Poland"=>1, "Italy"=>1, "Australia"=>1, "Chile"=>1, "Germany"=>7, "Indonesia"=>1}, "group_count"=>19}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/874002"], "description"=>"<p>(A) Histogram of the performance of 2,500 models on the “Cars vs. Planes” screening task (averaged over 10 random splits; error bars represent standard error of the mean). The top five performing models were selected for further analysis. (B) Performance of the top five models (1–5), and the performance achieved by averaging the five SVM kernels (red bar labelled “blend”) (C) Performance of the top five models (1–5) when trained with a different random initialization of filter weights (top) or with a different set of video clips taken from the “Law and Order” television program (bottom).</p>", "links"=>[], "tags"=>["petri"], "article_id"=>544462, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Nicolas Pinto", "David Doukhan", "James J. DiCarlo", "David D. Cox"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000579.g006", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_High_throughput_screening_in_the_8220_Law_and_Order_8221_petri_dish_/544462", "title"=>"High-throughput screening in the “Law and Order” petri dish.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-26 01:14:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/433811", "https://ndownloader.figshare.com/files/433849", "https://ndownloader.figshare.com/files/433880", "https://ndownloader.figshare.com/files/433912", "https://ndownloader.figshare.com/files/433939", "https://ndownloader.figshare.com/files/433975", "https://ndownloader.figshare.com/files/434008", "https://ndownloader.figshare.com/files/434030", "https://ndownloader.figshare.com/files/434057", "https://ndownloader.figshare.com/files/434104", "https://ndownloader.figshare.com/files/434189", "https://ndownloader.figshare.com/files/434342", "https://ndownloader.figshare.com/files/434399"], "description"=>"<div><p>While many models of biological object recognition share a common set of “broad-stroke” properties, the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model—e.g., the number of units per layer, the size of pooling kernels, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is typically large and the computational cost of evaluating one particular parameter set is high, the space of possible model instantiations goes largely unexplored. Thus, when a model fails to approach the abilities of biological visual systems, we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct “parts” have not been tuned correctly, assembled at sufficient scale, or provided with enough training. Here, we present a high-throughput approach to the exploration of such parameter sets, leveraging recent advances in stream processing hardware (high-end NVIDIA graphic cards and the PlayStation 3's IBM Cell Processor). In analogy to high-throughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. We show that this approach can yield significant, reproducible gains in performance across an array of basic object recognition tasks, consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature. As the scale of available computational power continues to expand, we argue that this approach has the potential to greatly accelerate progress in both artificial vision and our understanding of the computational underpinning of biological vision.</p></div>", "links"=>[], "tags"=>["high-throughput", "discovering", "forms", "biologically", "inspired"], "article_id"=>145594, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Nicolas Pinto", "David Doukhan", "James J. DiCarlo", "David D. Cox"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000579.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000579.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000579.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000579.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000579.s005", "https://dx.doi.org/10.1371/journal.pcbi.1000579.s006", "https://dx.doi.org/10.1371/journal.pcbi.1000579.s007", "https://dx.doi.org/10.1371/journal.pcbi.1000579.s008", "https://dx.doi.org/10.1371/journal.pcbi.1000579.s009", "https://dx.doi.org/10.1371/journal.pcbi.1000579.s010", "https://dx.doi.org/10.1371/journal.pcbi.1000579.s011", "https://dx.doi.org/10.1371/journal.pcbi.1000579.s012", "https://dx.doi.org/10.1371/journal.pcbi.1000579.s013"], "stats"=>{"downloads"=>11, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_High_Throughput_Screening_Approach_to_Discovering_Good_Forms_of_Biologically_Inspired_Visual_Representation/145594", "title"=>"A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-11-26 01:33:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/873786"], "description"=>"<p>The experiments described here consist of five phases. (A) First, a large collection of model instantiations are generated with randomly selected parameter values. (B) Each of these models then undergoes an unsupervised learning period, during which its filter kernels are adapted to spatio-temporal statistics of the video inputs, using a learning algorithm that is influenced by the particular parameter instantiation of that model. After the <i>Unsupervised Learning Phase</i> is complete, filter kernels are fixed, and (C) each model is subjected to a screening object recognition test, where labeled images are represented using each model instantiation, and these re-represented images are used to train an SVM to perform a simple two-class discrimination task. Performance of each candidate model is assessed using a standard cross-validation procedure. (D) From all of the model instantiations, the best are selected for further analysis. (E) Finally, these models are tested on other object recognition tasks.</p>", "links"=>[], "tags"=>["neuroscience/sensory systems", "computer science/natural and synthetic vision", "neuroscience/natural and synthetic vision"], "article_id"=>544241, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Nicolas Pinto", "David Doukhan", "James J. DiCarlo", "David D. Cox"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000579.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experimental_flow_/544241", "title"=>"Experimental flow.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-26 01:10:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/874074"], "description"=>"<p>Performance of the top five models from the <i>Screening Phase</i> on a variety of other object recognition challenges. Example images from each object recognition test are shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi-1000579-g005\" target=\"_blank\">Figure 5</a>. For each validation set, the performance (averaged over 10 random splits; error bars represent standard error of the mean) is first plotted for <i>V1-like</i> and <i>V1-like+</i> baseline models (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579-Pinto1\" target=\"_blank\">[10]</a>–<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579-Pinto3\" target=\"_blank\">[12]</a> for a detailed description of these two variants) (gray bars), and for five state-of-the-art vision systems (green bars): Scale Invariant Feature Transform (SIFT, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579-Lowe1\" target=\"_blank\">[40]</a>), Geometric Blur Descriptor (GB, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579-Berg1\" target=\"_blank\">[39]</a>), Pyramidal Histogram of Gradients (PHOG, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579-Bosch1\" target=\"_blank\">[37]</a>), Pyramidal Histogram of Words (PHOW, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579-Lazebnik1\" target=\"_blank\">[38]</a>), and a biologically-inspired hierarchical model (“Sparse Localized Features” SLF, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579-Mutch1\" target=\"_blank\">[8]</a>). Finally, performance of the five best models derived from the high-throughput screening approach presented in this paper (black bars), and the performance achieved by averaging the five SVM kernels (red bar labelled “blend”). In general, high-throughput-derived models outperformed the <i>V1-like</i> baseline models, and tended to outperform a variety of state-of-the-art systems from the literature. Model instantiation 3281 and the blend of all five top models uniformly produced the best results across all test sets considered here.</p>", "links"=>[], "tags"=>["neuroscience/sensory systems", "computer science/natural and synthetic vision", "neuroscience/natural and synthetic vision"], "article_id"=>544529, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Nicolas Pinto", "David Doukhan", "James J. DiCarlo", "David D. Cox"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000579.g007", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Validation_/544529", "title"=>"Validation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-26 01:15:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/873690"], "description"=>"<p>The system consists of three feedforward filtering layers, with the filters in each layer being applied across the previous layer. Red colored labels indicate a selection of configurable parameters (only a subset of parameters are shown).</p>", "links"=>[], "tags"=>["schematic", "diagram", "models"], "article_id"=>544142, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Nicolas Pinto", "David Doukhan", "James J. DiCarlo", "David D. Cox"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000579.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_schematic_diagram_of_the_system_architecture_of_the_family_of_models_considered_/544142", "title"=>"A schematic diagram of the system architecture of the family of models considered.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-26 01:09:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/873613"], "description"=>"<p>Our implemented performance speed-ups for a key filtering operation in our biologically-inspired model implementation. Performance and price are shown across a collection of different GPUs, relative to a commonly used MATLAB CPU-based implementation (using a single CPU core with the <i>filter2</i> function, which is coded in C++). We contrast this standard implementation with a multi-core MATLAB version, a highly-optimized C/SSE2 multi-core implementation on the same CPU, and highly-optimized GPU implementations. We have implemented speedups of over thousands of times with GPUs, resulting in qualitative changes in what kinds of model investigations are possible. More technical details and a throughout discussion of the computational framework enabling these speedups can be found in Supplemental <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579.s001\" target=\"_blank\">Figure S1</a> and Supplemental <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579.s012\" target=\"_blank\">Text S2</a>. * These costs are based on multi-GPU systems containing four GPUs in addition to the quad-core CPU (Q9450). ** These costs include both the hardware and MATLAB yearly licenses (based on an academic discount pricing, for one year).</p>", "links"=>[], "tags"=>["cpu", "gpu", "implementations"], "article_id"=>544069, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Nicolas Pinto", "David Doukhan", "James J. DiCarlo", "David D. Cox"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000579.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_and_cost_of_various_CPU_and_GPU_implementations_of_a_critical_component_of_our_model_family_/544069", "title"=>"Performance and cost of various CPU and GPU implementations of a critical component of our model family.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-26 01:07:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/873917"], "description"=>"<p>(A) A new set of rendered cars and planes composited onto random natural backgrounds. (B) Rendered boats and animals. (C) Rendered female and male faces. (D) A subset of the MultiPIE face test set <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579-Gross1\" target=\"_blank\">[27]</a> with the faces manually removed from the background, and composited onto random image backgrounds, with additional variation in position, scale, and planar rotation added.</p>", "links"=>[], "tags"=>["images", "validation"], "article_id"=>544374, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Nicolas Pinto", "David Doukhan", "James J. DiCarlo", "David D. Cox"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000579.g005", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_images_from_the_validation_test_sets_/544374", "title"=>"Examples of images from the validation test sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-26 01:12:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/873842"], "description"=>"<p>(A) Sequences of a rendered car undergoing a random walk through the possible range of rigid body movements. (B) A similar random walk with a rendered boat.</p>", "links"=>[], "tags"=>["frames"], "article_id"=>544306, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Nicolas Pinto", "David Doukhan", "James J. DiCarlo", "David D. Cox"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000579.g004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_video_frames_used_as_input_during_the_Unsupervised_Learning_Phase_/544306", "title"=>"Example video frames used as input during the <i>Unsupervised Learning Phase</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-26 01:11:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/874150"], "description"=>"<p>See Supplemental <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579.s011\" target=\"_blank\">Text S1</a> for an exhaustive description of the meaning of each parameter. The top five best performing models are plotted in red, with the other models overplotted in semi-transparent blue. The parameters considered in (A) and (B) show hints of a relationship between parameter value and inclusion in the top five. In (A) all of the five best models had the same value of the parameter, and in (B) best models were clustered in lower ranges of parameter value. (C) and (D) show parameters where the best models were distributed across a range of parameter values. Such examinations of parameter values are in no way conclusive, but can provide hints as to which parameters might be important for performance. See also Supplemental <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579.s013\" target=\"_blank\">Text S3</a>, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579.s009\" target=\"_blank\">Figures S9</a> and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000579#pcbi.1000579.s010\" target=\"_blank\">S10</a>.</p>", "links"=>[], "tags"=>["parameter", "arbitrarily-chosen"], "article_id"=>544607, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Nicolas Pinto", "David Doukhan", "James J. DiCarlo", "David D. Cox"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000579.g008", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distributions_of_screening_task_performance_as_a_function_of_parameter_values_for_four_arbitrarily_chosen_parameters_/544607", "title"=>"Distributions of screening task performance, as a function of parameter values for four arbitrarily-chosen parameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-26 01:16:47"}

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  • {"unique-ip"=>"13", "full-text"=>"8", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"5", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"3", "full-text"=>"2", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"4", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"8", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}

Relative Metric

{"start_date"=>"2009-01-01T00:00:00Z", "end_date"=>"2009-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[349, 614, 744, 851, 955, 1059, 1167, 1262, 1352, 1431, 1516, 1593, 1668, 1746, 1817, 1883, 1950, 2017, 2075, 2141, 2198, 2254, 2313, 2368, 2424, 2474, 2534, 2591, 2651, 2710, 2776, 2837, 2892, 2953, 3014, 3072, 3130, 3186, 3251, 3307, 3366, 3427, 3500, 3561, 3627, 3688, 3759, 3821, 3888, 3950, 4010, 4070, 4131, 4189, 4242, 4307, 4370, 4431, 4491, 4549, 4608]}, {"subject_area"=>"/Biology and life sciences/Molecular biology", "average_usage"=>[334, 613, 751, 863, 964, 1078, 1181, 1270, 1358, 1443, 1536, 1616, 1690, 1768, 1826, 1902, 1969, 2031, 2095, 2149, 2202, 2258, 2305, 2349, 2417, 2482, 2541, 2598, 2662, 2720, 2768, 2832, 2897, 2947, 2998, 3058, 3121, 3175, 3235, 3292, 3368, 3442, 3507, 3563, 3621, 3676, 3736, 3802, 3852, 3922, 3977, 4025, 4071, 4141, 4199, 4258, 4316, 4376, 4422, 4467, 4518]}, {"subject_area"=>"/Biology and life sciences/Neuroscience", "average_usage"=>[363, 598, 730, 824, 916, 1002, 1109, 1211, 1304, 1370, 1457, 1529, 1605, 1668, 1739, 1814, 1866, 1915, 1973, 2034, 2086, 2139, 2181, 2215, 2262, 2312, 2372, 2437, 2489, 2547, 2608, 2663, 2710, 2763, 2827, 2880, 2953, 3002, 3051, 3112, 3172, 3237, 3288, 3356, 3407, 3455, 3544, 3609, 3674, 3724, 3791, 3842, 3901, 3962, 4049, 4118, 4175, 4230, 4267, 4334, 4397]}, {"subject_area"=>"/Social sciences", "average_usage"=>[422, 688, 808, 943, 1033, 1166, 1296, 1375, 1481, 1566, 1664, 1767, 1833, 1924, 2011, 2092, 2138, 2185, 2279, 2344, 2397, 2433, 2482, 2545, 2590, 2638, 2703, 2792, 2853, 2908, 2965, 3028, 3092, 3163, 3258, 3351, 3404, 3455, 3501, 3562, 3631, 3699, 3762, 3822, 3872, 3921, 3978, 4020, 4098, 4158, 4223, 4292, 4383, 4450, 4549, 4609, 4649, 4705, 4784, 4849, 4934, 4981]}]}
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