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window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":7888218,"created_at":"2014-08-05T19:01:49.466-07:00","from_world_paper_id":125823225,"updated_at":"2024-11-12T23:37:47.194-08:00","_data":{"grobid_abstract":"We study the performance of a spiking network model based on integrate-and-fire neurons when performing a benchmark discrimination task. The task consists of determining the direction of moving dots in a noisy context. By varying the synaptic parameters of the integrate-and-fire neurons, we illustrate the counter-intuitive importance of the secondorder statistics (input noise) in improving the discrimination accuracy of the model. Surprisingly, we found that measuring the firing rate (FR) of a population of neurons considerably enhances the discrimination accuracy as well, in comparison with the firing rate of a single neuron. r","publication_date":"2005,,","publication_name":"Neurocomputing","grobid_abstract_attachment_id":"48301217"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Modelling a visual discrimination task","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [14753247]; 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.loswp.appleClientId = 'edu.academia.applesignon';</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":48301217,"attachmentType":"pdf"}"><img alt="First page of “Modelling a visual discrimination task”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/48301217/mini_magick20190204-5398-1av2fpn.png?1549295115" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.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">Modelling a visual discrimination task</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="14753247" href="https://independent.academia.edu/benoitgaillard"><img alt="Profile image of benoit gaillard" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />benoit gaillard</a></div><p class="ds-work-card--detail ds2-5-body-sm">2005, Neurocomputing</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":48301217,"attachmentType":"pdf","workUrl":"https://www.academia.edu/7888218/Modelling_a_visual_discrimination_task"}">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":48301217,"attachmentType":"pdf","workUrl":"https://www.academia.edu/7888218/Modelling_a_visual_discrimination_task"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="48301217" data-landing_url="https://www.academia.edu/7888218/Modelling_a_visual_discrimination_task" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="7888217" 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/7888217/Population_approach_to_a_neural_discrimination_task">Population approach to a neural discrimination task</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="14753247" href="https://independent.academia.edu/benoitgaillard">benoit gaillard</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Biological Cybernetics, 2006</p><p class="ds-related-work--abstract ds2-5-body-sm">This article gives insights into the possible neuronal processes involved in visual discrimination. We study the performance of a spiking network of Integrate-and-Fire (IF) neurons when performing a benchmark discrimination task. The task we adopted consists of determining the direction of moving dots in a noisy context using similar stimuli to those in the experiments of Newsome and colleagues. We present a neural model that performs the discrimination involved in this task. By varying the synaptic parameters of the IF neurons, we illustrate the counter-intuitive importance of the second-order statistics (input noise) in improving the discrimination accuracy of the model. We show that measuring the Firing Rate (FR) over a population enables the model to discriminate in realistic times, and even surprisingly significantly increases its discrimination accuracy over the single neuron case, despite the faster processing. We also show that increasing the input noise increases the discrimination accuracy but only at the expense of the speed at which we can read out the FR.</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":"Population approach to a neural discrimination task","attachmentId":48301259,"attachmentType":"pdf","work_url":"https://www.academia.edu/7888217/Population_approach_to_a_neural_discrimination_task","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/7888217/Population_approach_to_a_neural_discrimination_task"><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="98059628" 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/98059628/Spiking_Machine_Intelligence_What_we_can_learn_from_biology_and_how_spiking_Neural_Networks_can_help_to_improve_Machine_Learning">Spiking Machine Intelligence: What we can learn from biology and how spiking Neural Networks can help to improve Machine Learning</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="52676357" href="https://raifi.academia.edu/RichardGerum">Richard Gerum</a></div><p class="ds-related-work--metadata ds2-5-body-xs">ArXiv, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Up to now, modern Machine Learning is based on fitting high dimensional functions to enormous data sets, taking advantage of huge hardware resources. We show that biologically inspired neuron models such as the Integrate-and-Fire (LIF) neurons provide novel and efficient ways of information encoding. They can be integrated in Machine Learning models, and are a potential target to improve Machine Learning performance. Thus, we systematically analyze the LIF neuron. We start by deriving simple integration equations to which even a gradient can be assigned. Additionally, we prove that a Long-Short-Term-Memory unit can be tuned to show similar spiking properties. Additionally, LIF units are applied to an image classification task, trained with backpropagation. With this study we want to contribute to the current efforts to enhance Machine Intelligence by integrating principles from biology.</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 Machine Intelligence: What we can learn from biology and how spiking Neural Networks can help to improve Machine Learning","attachmentId":99514584,"attachmentType":"pdf","work_url":"https://www.academia.edu/98059628/Spiking_Machine_Intelligence_What_we_can_learn_from_biology_and_how_spiking_Neural_Networks_can_help_to_improve_Machine_Learning","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/98059628/Spiking_Machine_Intelligence_What_we_can_learn_from_biology_and_how_spiking_Neural_Networks_can_help_to_improve_Machine_Learning"><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="118089879" 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/118089879/Racing_to_Learn_Statistical_Inference_and_Learning_in_a_Single_Spiking_Neuron">Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron</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="32483888" href="https://westernsydney.academia.edu/Andre_van_Schaik">André van Schaik</a></div><p class="ds-related-work--metadata ds2-5-body-xs">arXiv (Cornell University), 2014</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":"Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron","attachmentId":113796532,"attachmentType":"pdf","work_url":"https://www.academia.edu/118089879/Racing_to_Learn_Statistical_Inference_and_Learning_in_a_Single_Spiking_Neuron","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/118089879/Racing_to_Learn_Statistical_Inference_and_Learning_in_a_Single_Spiking_Neuron"><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="93557108" 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/93557108/Simple_and_complex_spiking_neurons_perspectives_and_analysis_in_a_simple_STDP_scenario">Simple and complex spiking neurons: perspectives and analysis in a simple STDP scenario</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="77920737" href="https://independent.academia.edu/davidemanna1">davide manna</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Cornell University - arXiv, 2022</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":"Simple and complex spiking neurons: perspectives and analysis in a simple STDP scenario","attachmentId":96263038,"attachmentType":"pdf","work_url":"https://www.academia.edu/93557108/Simple_and_complex_spiking_neurons_perspectives_and_analysis_in_a_simple_STDP_scenario","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/93557108/Simple_and_complex_spiking_neurons_perspectives_and_analysis_in_a_simple_STDP_scenario"><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="20758944" 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/20758944/Temporal_Processing_in_a_Spiking_Model_of_the_Visual_System">Temporal Processing in a Spiking Model of the Visual System</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="33679528" href="https://warwick.academia.edu/ChristoPanchev">Christo Panchev</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Lecture Notes in Computer Science, 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":"Temporal Processing in a Spiking Model of the Visual System","attachmentId":41546241,"attachmentType":"pdf","work_url":"https://www.academia.edu/20758944/Temporal_Processing_in_a_Spiking_Model_of_the_Visual_System","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/20758944/Temporal_Processing_in_a_Spiking_Model_of_the_Visual_System"><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="90081145" 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/90081145/Spiking_Neural_Networks">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="109593380" href="https://univ-tlse2.academia.edu/OlivierMazet">Olivier Mazet</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2004</p><p class="ds-related-work--abstract ds2-5-body-sm">We study in this paper the effect of an unique initial stimulation on random recurrent networks of leaky integrate and fire neurons. Indeed given a stochastic connectivity this socalled spontaneous mode exhibits various non trivial dynamics. This study brings forward a mathematical formalism that allows us to examine the variability of the afterward dynamics according to the parameters of the weight distribution. Provided independence hypothesis (e.g. in the case of very large networks) we are able to compute the average number of neurons that fire at a given time – the spiking activity. In accordance with numerical simulations, we prove that this spiking activity reaches a steady-state, we characterize thissteady-state and explore the transients. 1</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 Neural Networks","attachmentId":93743185,"attachmentType":"pdf","work_url":"https://www.academia.edu/90081145/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/90081145/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="6" data-entity-id="79874905" 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/79874905/Fast_Temporal_Encoding_and_Decoding_with_Spiking_Neurons">Fast Temporal Encoding and Decoding with 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="40306422" href="https://independent.academia.edu/DavidHorn9">David Horn</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Neural Computation, 1998</p><p class="ds-related-work--abstract ds2-5-body-sm">We propose a simple theoretical structure of interacting integrate-and-fire neurons that can handle fast information processing and may account for the fact that only a few neuronal spikes suffice to transmit information in the brain. Using integrate-and-fire neurons that are subjected to individual noise and to a common external input, we calculate their first passage time (FPT), or interspike interval. We suggest using a population average for evaluating the FPT that represents the desired information. Instantaneous lateral excitation among these neurons helps the analysis. By employing a second layer of neurons with variable connections to the first layer, we represent the strength of the input by the number of output neurons that fire, thus decoding the temporal information. Such a model can easily lead to a logarithmic relation as in Weber&#39;s law. The latter follows naturally from information maximization if the input strength is statistically distributed according to an app...</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 Temporal Encoding and Decoding with Spiking Neurons","attachmentId":86443141,"attachmentType":"pdf","work_url":"https://www.academia.edu/79874905/Fast_Temporal_Encoding_and_Decoding_with_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/79874905/Fast_Temporal_Encoding_and_Decoding_with_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="7" data-entity-id="13229876" 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/13229876/Racing_to_learn_statistical_inference_and_learning_in_a_single_spiking_neuron_with_adaptive_kernels">Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels</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="37759746" href="https://independent.academia.edu/SAfshar2">S. Afshar</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32389428" href="https://westernsydney.academia.edu/JTapson">J. Tapson</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32483888" href="https://westernsydney.academia.edu/Andre_van_Schaik">André van Schaik</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Frontiers in Neuroscience, 2014</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":"Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels","attachmentId":45569026,"attachmentType":"pdf","work_url":"https://www.academia.edu/13229876/Racing_to_learn_statistical_inference_and_learning_in_a_single_spiking_neuron_with_adaptive_kernels","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/13229876/Racing_to_learn_statistical_inference_and_learning_in_a_single_spiking_neuron_with_adaptive_kernels"><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="71505375" 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/71505375/Spiking_neural_networks_for_computer_vision">Spiking neural networks for computer vision</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="276877" href="https://manchester.academia.edu/SteveFurber">Steve Furber</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Interface focus, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">State-of-the-art computer vision systems use frame-based cameras that sample the visual scene as a series of high-resolution images. These are then processed using convolutional neural networks using neurons with continuous outputs. Biological vision systems use a quite different approach, where the eyes (cameras) sample the visual scene continuously, often with a non-uniform resolution, and generate neural spike events in response to changes in the scene. The resulting spatio-temporal patterns of events are then processed through networks of spiking neurons. Such event-based processing offers advantages in terms of focusing constrained resources on the most salient features of the perceived scene, and those advantages should also accrue to engineered vision systems based upon similar principles. Event-based vision sensors, and event-based processing exemplified by the SpiNNaker (Spiking Neural Network Architecture) machine, can be used to model the biological vision pathway at vari...</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 neural networks for computer vision","attachmentId":80818916,"attachmentType":"pdf","work_url":"https://www.academia.edu/71505375/Spiking_neural_networks_for_computer_vision","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/71505375/Spiking_neural_networks_for_computer_vision"><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="19743595" 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/19743595/Biologically_inspired_features_in_spiking_neural_networks">Biologically inspired features in 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="40400767" href="https://independent.academia.edu/DStroobandt">Dirk Stroobandt</a></div><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":"Biologically inspired features in spiking neural networks","attachmentId":42034918,"attachmentType":"pdf","work_url":"https://www.academia.edu/19743595/Biologically_inspired_features_in_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/19743595/Biologically_inspired_features_in_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></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":48301217,"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":48301217,"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_48301217" 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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