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(PDF) An efficient simulation environment for modeling large-scale cortical processing
<!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="RjJuokFUiSoqaeFTP9xDmAEWT9q4JXPf_BSJDYSv24GDONlAJ9PHwR7FtMjqPNyxVIJ6Yx_-PQhGo5F2w4Tuog" /> <meta name="citation_title" content="An efficient simulation environment for modeling large-scale cortical processing" /> <meta name="citation_publication_date" content="2011/01/01" /> <meta name="citation_journal_title" content="Frontiers in neuroinformatics" /> <meta name="citation_author" content="Micah Richert" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/30267591/An_efficient_simulation_environment_for_modeling_large_scale_cortical_processing" /> <meta name="twitter:title" content="An efficient simulation environment for modeling large-scale cortical processing" /> <meta name="twitter:description" content="We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich" /> <meta name="twitter:image" content="http://a.academia-assets.com/images/twitter-card.jpeg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/30267591/An_efficient_simulation_environment_for_modeling_large_scale_cortical_processing" /> <meta property="og:title" content="An efficient simulation environment for modeling large-scale cortical processing" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich" /> <meta property="article:author" content="https://independent.academia.edu/MicahRichert" /> <meta name="description" content="We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich" /> <title>(PDF) An efficient simulation environment for modeling large-scale cortical processing</title> <link rel="canonical" href="https://www.academia.edu/30267591/An_efficient_simulation_environment_for_modeling_large_scale_cortical_processing" /> <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 = '075e914b9e16164113b5b9afd7238a56a7292942'; 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(1740062624000); window.Aedu.timeDifference = new Date().getTime() - 1740062624000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. It uses a standard network construction interface. The simulator allows for execution on either GPUs or CPUs. The simulator, which is written in C/C++, allows for both fine grain and coarse grain specificity of a host of parameters. We demonstrate the ease of use and computational efficiency of this model by implementing a large-scale model of cortical areas V1, V4, and area MT. The complete model, which has 138,240 neurons and approximately 30 million synapses, runs in real-time on an off-the-shelf GPU. The simulator source code, as well as the source code for the cortical model examples is publicly available.","author":[{"@context":"https://schema.org","@type":"Person","name":"Micah Richert","url":"https://independent.academia.edu/MicahRichert"}],"contributor":[],"dateCreated":"2016-12-05","dateModified":"2016-12-05","datePublished":"2011-01-01","headline":"An efficient simulation environment for modeling large-scale cortical processing","image":"https://attachments.academia-assets.com/50734120/thumbnails/1.jpg","inLanguage":"en","keywords":["Computational Neuroscience","Simulation"],"publication":"Frontiers in 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window.loswp.work = {"work":{"id":30267591,"created_at":"2016-12-05T19:18:51.191-08:00","from_world_paper_id":159568028,"updated_at":"2021-01-18T02:54:10.562-08:00","_data":{"abstract":"We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. It uses a standard network construction interface. The simulator allows for execution on either GPUs or CPUs. The simulator, which is written in C/C++, allows for both fine grain and coarse grain specificity of a host of parameters. We demonstrate the ease of use and computational efficiency of this model by implementing a large-scale model of cortical areas V1, V4, and area MT. The complete model, which has 138,240 neurons and approximately 30 million synapses, runs in real-time on an off-the-shelf GPU. The simulator source code, as well as the source code for the cortical model examples is publicly available.","publication_date":"2011,,","publication_name":"Frontiers in neuroinformatics"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"An efficient simulation environment for modeling large-scale cortical processing","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [57694469]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; 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"><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":50734120,"attachmentType":"pdf"}"><img alt="First page of “An efficient simulation environment for modeling large-scale cortical processing”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/50734120/mini_magick20190127-19537-1qul7zn.png?1548647625" /><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">An efficient simulation environment for modeling large-scale cortical processing</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></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2011, Frontiers in neuroinformatics</p><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">16 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 = 30267591; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=30267591"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); 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">We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. It uses a standard network construction interface. The simulator allows for execution on either GPUs or CPUs. The simulator, which is written in C/C++, allows for both fine grain and coarse grain specificity of a host of parameters. We demonstrate the ease of use and computational efficiency of this model by implementing a large-scale model of cortical areas V1, V4, and area MT. The complete model, which has 138,240 neurons and approximately 30 million synapses, runs in real-time on an off-the-shelf GPU. The simulator source code, as well as the source code for the cortical model examples is 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":50734120,"attachmentType":"pdf","workUrl":"https://www.academia.edu/30267591/An_efficient_simulation_environment_for_modeling_large_scale_cortical_processing"}">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":50734120,"attachmentType":"pdf","workUrl":"https://www.academia.edu/30267591/An_efficient_simulation_environment_for_modeling_large_scale_cortical_processing"}"><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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Compared to other highly-parallel systems, GPU-accelerated solutions have the advantage of a relatively low cost and a great versatility, thanks also to the possibility of using the CUDA-C/C++ programming languages. NeuronGPU is a GPU library for large-scale simulations of spiking neural network models, written in the C++ and CUDA-C++ programming languages, based on a novel spike-delivery algorithm. This library includes simple LIF (leaky-integrate-and-fire) neuron models as well as several multisynapse AdEx (adaptive-exponential-integrate-and-fire) neuron models with current or conductance based synapses, different types of spike generators, tools for recording spikes, state variables and parameters, and it supports user-definable models. The numerical solution of the differential equations of the dynamics of the AdEx models ...</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":"Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs","attachmentId":78451453,"attachmentType":"pdf","work_url":"https://www.academia.edu/67733647/Fast_Simulations_of_Highly_Connected_Spiking_Cortical_Models_Using_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-wsj-grid-card-view-pdf" href="https://www.academia.edu/67733647/Fast_Simulations_of_Highly_Connected_Spiking_Cortical_Models_Using_GPUs"><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-wsj-grid-card" data-collection-position="1" data-entity-id="108066054" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/108066054/SPAYK_An_environment_for_spiking_neural_network_simulation">SPAYK: An environment for spiking neural network simulation</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="49307795" href="https://independent.academia.edu/AtasoyAyten">Ayten Atasoy</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Turkish Journal of Electrical Engineering and Computer Sciences, 2023</p><p class="ds-related-work--abstract ds2-5-body-sm">In research areas such as mobile robotics and computer vision, energy and computational efficiency have become critical. This has greatly increased interest in high-efficiency neuromorphic hardware and spiking neural networks. Because neuromorphic hardware is not yet widely available, spiking neural network studies are conducted by simulations. There are numerous simulators available today, each designed for a specific purpose. In this paper, a novel and opensource package (SPAYK) for simulating spiking neural networks is presented. SPAYK has been proposed to speed up spiking neural network research. In the majority of simulators, networks are expressed with differential equations and require advanced neuroscience knowledge since such simulators are generally designed for brain and neuroscience research. SPAYK, on the other hand, is specifically designed as a framework to easily design spiking neural networks for practical problems. SPAYK is an easy-to-use Python package. There are three fundamental classes in the core: the model class for creating neuron groups, the organization class for simulating tissues, and the learning class for synaptic plasticity. While developing and testing the SPAYK environment, various experiments were carried out. This study includes three of these experiments. In the first experiment, we investigated the behavior of a group of Izhikevich neurons for visual stimuli. Also, a single Izhikevich neuron has been trained to respond to a particular label in a supervised manner with synaptic plasticity. In the second experiment, a well-known experiment was repeated to validate SPAYK. In this experiment, a neuron trained by synaptic plasticity can recognize repetitive patterns in a spike train. In the third experiment, a similar neuron was simulated with stimuli with multiple labels adapted from the MNIST dataset. It has been shown that the neuron can classify a particular label by synaptic plasticity. All these experiments and the SPAYK environment are presented as open-source tools.</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":"SPAYK: An environment for spiking neural network simulation","attachmentId":106551119,"attachmentType":"pdf","work_url":"https://www.academia.edu/108066054/SPAYK_An_environment_for_spiking_neural_network_simulation","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-wsj-grid-card-view-pdf" href="https://www.academia.edu/108066054/SPAYK_An_environment_for_spiking_neural_network_simulation"><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-wsj-grid-card" data-collection-position="2" data-entity-id="30267595" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/30267595/Neuromorphic_modeling_abstractions_and_simulation_of_large_scale_cortical_networks">Neuromorphic modeling abstractions and simulation of large-scale cortical networks</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="57694469" href="https://independent.academia.edu/MicahRichert">Micah Richert</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2011 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2011</p><p class="ds-related-work--abstract ds2-5-body-sm">Biological neural systems are well known for their robust and power-efficient operation in highly noisy environments. We outline key modeling abstractions for the brain and focus on spiking neural network models. We discuss aspects of neuronal processing and computational issues related to modeling these processes. Although many of these algorithms can be efficiently realized in specialized hardware, we present a case study of simulation of the visual cortex using a GPU based simulation environment that is readily usable by neuroscientists and computer scientists and efficient enough to construct very large networks comparable to brain networks.</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":"Neuromorphic modeling abstractions and simulation of large-scale cortical networks","attachmentId":50734129,"attachmentType":"pdf","work_url":"https://www.academia.edu/30267595/Neuromorphic_modeling_abstractions_and_simulation_of_large_scale_cortical_networks","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-wsj-grid-card-view-pdf" href="https://www.academia.edu/30267595/Neuromorphic_modeling_abstractions_and_simulation_of_large_scale_cortical_networks"><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-wsj-grid-card" data-collection-position="3" data-entity-id="52084461" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/52084461/Real_time_cortical_simulation_on_neuromorphic_hardware">Real-time cortical simulation on neuromorphic hardware</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="21114" href="https://manchester.academia.edu/AndrewGait">Andrew Gait</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences</p><p class="ds-related-work--abstract ds2-5-body-sm">Real-time simulation of a large-scale biologically representative spiking neural network is presented, through the use of a heterogeneous parallelization scheme and SpiNNaker neuromorphic hardware. A published cortical microcircuit model is used as a benchmark test case, representing ≈1 mm 2 of early sensory cortex, containing 77 k neurons and 0.3 billion synapses. This is the first hard real-time simulation of this model, with 10 s of biological simulation time executed in 10 s wall-clock time. This surpasses best-published efforts on HPC neural simulators (3 × slowdown) and GPUs running optimized spiking neural network (SNN) libraries (2 × slowdown). Furthermore, the presented approach indicates that real-time processing can be maintained with increasing SNN size, breaking the communication barrier incurred by traditional computing machinery. Model results are compared to an established HPC simulator baseline to verify simulation correctness, comparing well across a range of stati...</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":"Real-time cortical simulation on neuromorphic hardware","attachmentId":69512096,"attachmentType":"pdf","work_url":"https://www.academia.edu/52084461/Real_time_cortical_simulation_on_neuromorphic_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-wsj-grid-card-view-pdf" href="https://www.academia.edu/52084461/Real_time_cortical_simulation_on_neuromorphic_hardware"><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-wsj-grid-card" data-collection-position="4" data-entity-id="50675356" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/50675356/Efficient_simulation_of_large_scale_Spiking_Neural_Networks_using_CUDA_graphics_processors">Efficient simulation of large-scale Spiking Neural Networks using CUDA graphics processors</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="86587124" href="https://uci.academia.edu/ANicolau">Alex Nicolau</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2009 International Joint Conference on Neural Networks, 2009</p><p class="ds-related-work--abstract ds2-5-body-sm">Neural network simulators that take into account the spiking behavior of neurons are useful for studying brain mechanisms and for engineering applications. Spiking Neural Network (SNN) simulators have been traditionally simulated on large-scale clusters, super-computers, or on dedicated hardware architectures. Alternatively, Graphics Processing Units (GPUs) can provide a low-cost, programmable, and highperformance computing platform for simulation of SNNs. In this paper we demonstrate an efficient, Izhikevich neuron based large-scale SNN simulator that runs on a single GPU. The GPU-SNN model (running on an NVIDIA GTX-280 with 1GB of memory), is up to 26 times faster than a CPU version for the simulation of 100K neurons with 50 Million synaptic connections, firing at an average rate of 7Hz. For simulation of 100K neurons with 10 Million synaptic connections, the GPU-SNN model is only 1.5 times slower than real-time. Further, we present a collection of new techniques related to parallelism extraction, mapping of irregular communication, and compact network representation for effective simulation of SNNs on GPUs. The fidelity of the simulation results were validated against CPU simulations using firing rate, synaptic weight distribution, and inter-spike interval analysis. We intend to make our simulator available to the modeling community so that researchers will have easy access to large-scale SNN simulations.</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":"Efficient simulation of large-scale Spiking Neural Networks using CUDA graphics processors","attachmentId":68565943,"attachmentType":"pdf","work_url":"https://www.academia.edu/50675356/Efficient_simulation_of_large_scale_Spiking_Neural_Networks_using_CUDA_graphics_processors","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-wsj-grid-card-view-pdf" href="https://www.academia.edu/50675356/Efficient_simulation_of_large_scale_Spiking_Neural_Networks_using_CUDA_graphics_processors"><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-wsj-grid-card" data-collection-position="5" data-entity-id="23645446" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/23645446/Building_simulating_and_visualizing_large_spiking_neural_networks_with_NeuralSyns">Building, simulating and visualizing large spiking neural networks with NeuralSyns</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32980341" href="https://up-pt.academia.edu/PauloAguiar">Paulo Aguiar</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Neurocomputing, 2014</p><p class="ds-related-work--abstract ds2-5-body-sm">The understanding that many neuronal functional properties are the result of complex interactions within large populations of neurons leads to the development and analysis of large network models. In these models, specificities such as connectivity and architecture of the heterogeneous neuronal population are at least as important as the individual neuronal dynamics in shaping the population's computational power. Unfortunately, designing, building, visualizing and simulating large network models focused on architecture complexity is not straightforward with most neuronal simulation tools. Visualization in particular, either for model construction debugging, online simulation analysis or even classroom demonstrations, is virtually unsupported in neuronal simulators targeting large network models. Here we present the simulation environment NeuralSyns, capable of handling models with up to 10 7 synapses, which offers (i) use of statistical descriptions to define and build large networks; (ii) GUI tool to construct complex architectures without the need to write code; (iii) information-rich graphical representations of the model architecture and dynamical state, before and during the simulations; (iv) simulation engine with compact and lean source code, fully commented, and simple to modify/ expand; (v) possibility to add new models for neurons, synapses and plasticity dynamics. NeuralSyns is written in C/C þ þ and relies on OpenGL code for its graphical engine. The use of specific OpenGL strategies removes rendering burden from the CPU (central processing unit) and places it in the GPU (graphics processing unit), reducing the simulation slowdown due to the graphical representation.</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":"Building, simulating and visualizing large spiking neural networks with NeuralSyns","attachmentId":44055927,"attachmentType":"pdf","work_url":"https://www.academia.edu/23645446/Building_simulating_and_visualizing_large_spiking_neural_networks_with_NeuralSyns","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-wsj-grid-card-view-pdf" href="https://www.academia.edu/23645446/Building_simulating_and_visualizing_large_spiking_neural_networks_with_NeuralSyns"><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-wsj-grid-card" data-collection-position="6" data-entity-id="15029358" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/15029358/PAX_A_mixed_hardware_software_simulation_platform_for_spiking_neural_networks">PAX: A mixed hardware/software simulation platform for spiking neural networks</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="34038676" href="https://independent.academia.edu/AlainDestexhe">Alain Destexhe</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Neural Networks, 2010</p><p class="ds-related-work--abstract ds2-5-body-sm">Many hardware-based solutions now exist for the simulation of bio-like neural networks. Less conventional than software-based systems, these types of simulator generally combine digital and analog forms of computation. In this paper we present a mixed hardware-software platform, specifically designed for the simulation of spiking neural networks, using conductance-based models of neurons and synaptic connections with dynamic adaptation rules (Spike-Timing Dependent Plasticity). The neurons and networks are configurable, and are computed in "biological real time" by which we mean that the difference between simulated time and simulation time is guaranted lower than 50µs. After presenting the issues and context involved in the design and use of hardware-based spiking neural networks, we describe the analog neuromimetic integrated circuits which form the core of the platform. We then explain the organisation and computation principles of the modules within the platform, and present experimental results which validate the system. Designed as a tool for computational neuroscience, the platform is exploited in collaborative research projects together with neurobiology and computer science partners.</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":"PAX: A mixed hardware/software simulation platform for spiking neural networks","attachmentId":43645801,"attachmentType":"pdf","work_url":"https://www.academia.edu/15029358/PAX_A_mixed_hardware_software_simulation_platform_for_spiking_neural_networks","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-wsj-grid-card-view-pdf" href="https://www.academia.edu/15029358/PAX_A_mixed_hardware_software_simulation_platform_for_spiking_neural_networks"><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-wsj-grid-card" data-collection-position="7" data-entity-id="67733638" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/67733638/A_new_GPU_library_for_fast_simulation_of_large_scale_networks_of_spiking_neurons">A new GPU library for fast simulation of large-scale networks of spiking neurons</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="40755521" href="https://independent.academia.edu/FSimula">Francesco Simula</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Over the past decade there has been a growing interest in the development of parallel hardware systems for simulating large-scale networks of spiking neurons. Compared to other highly-parallel systems, GPU-accelerated solutions have the advantage of a relatively low cost and a great versatility, thanks also to the possibility of using the CUDA-C/C++ programming languages. NeuronGPU is a GPU library for large-scale simulations of spiking neural network models, written in the C++ and CUDA-C++ programming languages, based on a novel spike-delivery algorithm. This library includes simple LIF (leaky-integrate-and-fire) neuron models as well as several multisynapse AdEx (adaptive-exponential-integrate-and-fire) neuron models with current or conductance based synapses, user definable models and different devices. The numerical solution of the differential equations of the dynamics of the AdEx models is performed through a parallel implementation, written in CUDA-C++, of the fifth-order Run...</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 new GPU library for fast simulation of large-scale networks of spiking neurons","attachmentId":78451409,"attachmentType":"pdf","work_url":"https://www.academia.edu/67733638/A_new_GPU_library_for_fast_simulation_of_large_scale_networks_of_spiking_neurons","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-wsj-grid-card-view-pdf" href="https://www.academia.edu/67733638/A_new_GPU_library_for_fast_simulation_of_large_scale_networks_of_spiking_neurons"><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-wsj-grid-card" data-collection-position="8" data-entity-id="30182747" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/30182747/CARLsim_3_A_User_Friendly_and_Highly_Optimized_Library_for_the_Creation_of_Neurobiologically_Detailed_Spiking_Neural_Networks">CARLsim 3: A User-Friendly and Highly Optimized Library for the Creation of Neurobiologically Detailed Spiking Neural Networks</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="57466114" href="https://washington.academia.edu/MichaelBeyeler">Michael Beyeler</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="57691442" href="https://independent.academia.edu/TingShuoChou">Ting-Shuo Chou</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Spiking neural network (SNN) models describe key aspects of neural function in a computationally efficient manner and have been used to construct large-scale brain models. Large-scale SNNs are challenging to implement, as they demand high-bandwidth communication, a large amount of memory, and are computationally intensive. Additionally, tuning parameters of these models becomes more difficult and time-consuming with the addition of biologically accurate descriptions. To meet these challenges, we have developed CARLsim 3, a user-friendly, GPU-accelerated SNN library written in C/C++ that is capable of simulating biologically detailed neural models. The present release of CARLsim provides a number of improvements over our prior SNN library to allow the user to easily analyze simulation data, explore synaptic plasticity rules, and automate parameter tuning. In the present paper, we provide examples and performance benchmarks highlighting the library's features.</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":"CARLsim 3: A User-Friendly and Highly Optimized Library for the Creation of Neurobiologically Detailed Spiking Neural Networks","attachmentId":50641621,"attachmentType":"pdf","work_url":"https://www.academia.edu/30182747/CARLsim_3_A_User_Friendly_and_Highly_Optimized_Library_for_the_Creation_of_Neurobiologically_Detailed_Spiking_Neural_Networks","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-wsj-grid-card-view-pdf" href="https://www.academia.edu/30182747/CARLsim_3_A_User_Friendly_and_Highly_Optimized_Library_for_the_Creation_of_Neurobiologically_Detailed_Spiking_Neural_Networks"><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-wsj-grid-card" data-collection-position="9" data-entity-id="13740034" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/13740034/Simulation_of_networks_of_spiking_neurons_A_review_of_tools_and_strategies">Simulation of networks of spiking neurons: A review of tools and strategies</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="34038676" href="https://independent.academia.edu/AlainDestexhe">Alain Destexhe</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32861011" href="https://kth.academia.edu/MikaelDjurfeldt">Mikael Djurfeldt</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Computational Neuroscience, 2007</p><p class="ds-related-work--abstract ds2-5-body-sm">We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.</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":"Simulation of networks of spiking neurons: A review of tools and strategies","attachmentId":45003149,"attachmentType":"pdf","work_url":"https://www.academia.edu/13740034/Simulation_of_networks_of_spiking_neurons_A_review_of_tools_and_strategies","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-wsj-grid-card-view-pdf" href="https://www.academia.edu/13740034/Simulation_of_networks_of_spiking_neurons_A_review_of_tools_and_strategies"><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></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":50734120,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":50734120,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_50734120" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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