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<!DOCTYPE html> <html > <head> <meta charset="utf-8"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <meta content="width=device-width, initial-scale=1" name="viewport"> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs"> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="R4cgVwnfKEM36tGRpIO5XS21uEWIMqpy_qI7uE8fuCxriAXu-mthNbIxjQoKiKjYiO5Dkh2mX71fjRpnaoLXVQ" /> <meta name="citation_title" content="Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex" /> <meta name="citation_author" content="Micah Richert" /> <meta name="citation_author" content="Michael Beyeler" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/30182869/Efficient_Spiking_Neural_Network_Model_of_Pattern_Motion_Selectivity_in_Visual_Cortex" /> <meta name="twitter:title" content="Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex" /> <meta name="twitter:description" content="Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance" /> <meta name="twitter:image" content="https://0.academia-photos.com/57466114/15077680/15795261/s200_michael.beyeler.jpg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/30182869/Efficient_Spiking_Neural_Network_Model_of_Pattern_Motion_Selectivity_in_Visual_Cortex" /> <meta property="og:title" content="Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance" /> <meta property="article:author" content="https://washington.academia.edu/MichaelBeyeler" /> <meta property="article:author" content="https://independent.academia.edu/MicahRichert" /> <meta name="description" content="Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance" /> <title>(PDF) Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex</title> <link rel="canonical" href="https://www.academia.edu/30182869/Efficient_Spiking_Neural_Network_Model_of_Pattern_Motion_Selectivity_in_Visual_Cortex" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = 'b092bf3a3df71cf13feee7c143e83a57eb6b94fb'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1739854713000); window.Aedu.timeDifference = new Date().getTime() - 1739854713000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation out-performs a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40×40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available.","author":[{"@context":"https://schema.org","@type":"Person","name":"Michael Beyeler","url":"https://washington.academia.edu/MichaelBeyeler"},{"@context":"https://schema.org","@type":"Person","name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"}],"contributor":[{"@context":"https://schema.org","@type":"Person","name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"}],"dateCreated":"2016-11-30","dateModified":"2016-12-05","headline":"Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex","image":"https://attachments.academia-assets.com/50641757/thumbnails/1.jpg","inLanguage":"en","keywords":["Computational Modeling","Computational Modelling","GPU Computing","Motion Analysis","GPGPU (General Purpose GPU) Programming","Visual Cortex","Primary visual cortex","Spiking Neural Networks","Visual Motion","Visual Motion Perception"],"publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"washington"},{"@context":"https://schema.org","@type":"EducationalOrganization","name":null}],"thumbnailUrl":"https://attachments.academia-assets.com/50641757/thumbnails/1.jpg","url":"https://www.academia.edu/30182869/Efficient_Spiking_Neural_Network_Model_of_Pattern_Motion_Selectivity_in_Visual_Cortex"}</script><style type="text/css">@media(max-width: 567px){:root{--token-mode: Rebrand;--dropshadow: 0 2px 4px 0 #22223340;--primary-brand: #0645b1;--error-dark: #b60000;--success-dark: #05b01c;--inactive-fill: #ebebee;--hover: #0c3b8d;--pressed: #082f75;--button-primary-fill-inactive: #ebebee;--button-primary-fill: #0645b1;--button-primary-text: #ffffff;--button-primary-fill-hover: 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A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation out-performs a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40×40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available."},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex","broadcastable":true,"draft":false,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [57694469,57466114]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card ds-work-card--no-bottom-spacing"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":50641757,"attachmentType":"pdf"}"><img alt="First page of “Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/50641757/mini_magick20190128-1632-6kvjey.png?1548696306" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="57694469" href="https://independent.academia.edu/MicahRichert"><img alt="Profile image of Micah Richert" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Micah Richert</a><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="57466114" href="https://washington.academia.edu/MichaelBeyeler"><img alt="Profile image of Michael Beyeler" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/57466114/15077680/15795261/s65_michael.beyeler.jpg" />Michael Beyeler</a></div><div class="ds-work-card--detail"><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">20 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 30182869; 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if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation out-performs a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40×40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":50641757,"attachmentType":"pdf","workUrl":"https://www.academia.edu/30182869/Efficient_Spiking_Neural_Network_Model_of_Pattern_Motion_Selectivity_in_Visual_Cortex"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":50641757,"attachmentType":"pdf","workUrl":"https://www.academia.edu/30182869/Efficient_Spiking_Neural_Network_Model_of_Pattern_Motion_Selectivity_in_Visual_Cortex"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="55594918" href="https://independent.academia.edu/MHulle">Marc Van Hulle</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Vision, 2010</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A cortical architecture on parallel hardware for motion processing in real time","attachmentId":49904903,"attachmentType":"pdf","work_url":"https://www.academia.edu/29461287/A_cortical_architecture_on_parallel_hardware_for_motion_processing_in_real_time","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" 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Nowotny</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2018</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Brian2GeNN: a system for accelerating a large variety of spiking neural networks with graphics hardware","attachmentId":84879922,"attachmentType":"pdf","work_url":"https://www.academia.edu/77543599/Brian2GeNN_a_system_for_accelerating_a_large_variety_of_spiking_neural_networks_with_graphics_hardware","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" 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class="ds-related-work--metadata ds2-5-body-xs">The 2012 International Joint Conference on Neural Networks (IJCNN), 2012</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Implementation of configurable and multipurpose spiking neural networks on GPUs","attachmentId":47394782,"attachmentType":"pdf","work_url":"https://www.academia.edu/10424121/Implementation_of_configurable_and_multipurpose_spiking_neural_networks_on_GPUs","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/10424121/Implementation_of_configurable_and_multipurpose_spiking_neural_networks_on_GPUs"><span 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ds2-5-body-link" href="https://www.academia.edu/127247530/High_performance_computing_for_systems_of_spiking_neurons">High-performance computing for systems of spiking neurons</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="276877" href="https://manchester.academia.edu/SteveFurber">Steve Furber</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2006</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"High-performance computing for systems of spiking neurons","attachmentId":121008901,"attachmentType":"pdf","work_url":"https://www.academia.edu/127247530/High_performance_computing_for_systems_of_spiking_neurons","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span 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Simoncelli</a></div><p class="ds-related-work--metadata ds2-5-body-xs">1998</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Model of Neuronal Responses in Visual","attachmentId":119067259,"attachmentType":"pdf","work_url":"https://www.academia.edu/124933956/A_Model_of_Neuronal_Responses_in_Visual","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/124933956/A_Model_of_Neuronal_Responses_in_Visual"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="14" data-entity-id="94243550" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/94243550/Brian2GeNN_accelerating_spiking_neural_network_simulations_with_graphics_hardware">Brian2GeNN: accelerating spiking neural network simulations with graphics hardware</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="31242626" href="https://sussex.academia.edu/ThomasNowotny">Thomas Nowotny</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Scientific Reports, 2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Brian2GeNN: accelerating spiking neural network simulations with 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2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Spiking Associative Memory for Spatio-Temporal Patterns","attachmentId":95422911,"attachmentType":"pdf","work_url":"https://www.academia.edu/92405681/Spiking_Associative_Memory_for_Spatio_Temporal_Patterns","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/92405681/Spiking_Associative_Memory_for_Spatio_Temporal_Patterns"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container 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Linares-Barranco</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Frontiers in Neuroscience, 2012</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Comparison between Frame-Constrained Fix-Pixel-Value and Frame-Free Spiking-Dynamic-Pixel ConvNets for Visual Processing","attachmentId":74998562,"attachmentType":"pdf","work_url":"https://www.academia.edu/62162798/Comparison_between_Frame_Constrained_Fix_Pixel_Value_and_Frame_Free_Spiking_Dynamic_Pixel_ConvNets_for_Visual_Processing","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" 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