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Michael Herrmann</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://edinburgh.academia.edu/">University of Edinburgh</a>, <a class="u-tcGrayDarker" href="https://edinburgh.academia.edu/Departments/Informatics/Documents">Informatics</a>, <span class="u-tcGrayDarker">Faculty Member</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="J. 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href="https://uni-freiburg.academia.edu/httpswwwuniklinikfreiburgdeneurozentrumforschungprofdrrumyanakristevahtml"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://uni-freiburg.academia.edu/httpswwwuniklinikfreiburgdeneurozentrumforschungprofdrrumyanakristevahtml">Rumyana Kristeva</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Albert-Ludwigs-Universit盲t Freiburg</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://uzh.academia.edu/MarieclaudeHeppreymond"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a 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id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by J. Michael Herrmann</h3></div><div class="js-work-strip profile--work_container" data-work-id="20282108"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282108/Structured_control_from_self_organizing_arm_movements"><img alt="Research paper thumbnail of Structured control from self-organizing arm movements" class="work-thumbnail" src="https://attachments.academia-assets.com/41344132/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282108/Structured_control_from_self_organizing_arm_movements">Structured control from self-organizing arm movements</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/GeorgMartius">Georg Martius</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://edinburgh.academia.edu/DrMichaelHerrmann">J. Michael Herrmann</a></span></div><div class="wp-workCard_item"><span>BMC Neuroscience</span><span>, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8fb45f3323b09959609aabe8847c2828" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41344132,"asset_id":20282108,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41344132/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282108"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282108"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282108; 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A typical setup to determine priming effects is the identity priming paradigm as it is shown in fig. 1. A person fixates a cross on a screen and is presented two stimuli at a time. Some feature (here: color) determines the discrimination between target and distractor. The green stimulus is the target and the red one the distractor.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ae052d1982f9dfdb60692fc02e6a6245" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737146,"asset_id":2783326,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737146/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783326"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783326"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783326; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783326]").text(description); $(".js-view-count[data-work-id=2783326]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783326; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783326']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ae052d1982f9dfdb60692fc02e6a6245" } } $('.js-work-strip[data-work-id=2783326]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783326,"title":"A Computational Account of the Negative Priming Effect","internal_url":"https://www.academia.edu/2783326/A_Computational_Account_of_the_Negative_Priming_Effect","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. 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This is not only because systems become more lifelike, but also since self-organisation may help in reducing the design efforts in creating complex behaviour systems. We consider agents under the close sensorimotor coupling paradigm with a certain cognitive ability realised by an internal forward model.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783325"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783325"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783325; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783325]").text(description); $(".js-view-count[data-work-id=2783325]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783325; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783325']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=2783325]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783325,"title":"Self-exploration in a DS Approach to Early Robot Development","internal_url":"https://www.academia.edu/2783325/Self_exploration_in_a_DS_Approach_to_Early_Robot_Development","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. 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The model consists of three layers of pulse-coupled leaky integrate-and-fire neurons. Features are encoded by the location of sustained activity in the subordinate layers. The feature layers are excitatorily coupled to a superordinate layer that represents combinations of features by means of an oscillatory dynamics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6954ecfaa9a0d5c6fdd2a17554d4c914" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737157,"asset_id":2783324,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737157/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783324"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783324"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783324; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783324]").text(description); $(".js-view-count[data-work-id=2783324]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783324; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783324']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6954ecfaa9a0d5c6fdd2a17554d4c914" } } $('.js-work-strip[data-work-id=2783324]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783324,"title":"A feature-binding model with localized excitations","internal_url":"https://www.academia.edu/2783324/A_feature_binding_model_with_localized_excitations","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. 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This includes the selection of behaviorally relevant perceptual features, the adaptation of control mechanisms and the storage and the recall of behavioral episodes for planning and execution.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="00cffd2ba2cea28abf544d3d4e7c316e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737106,"asset_id":2783323,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737106/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783323"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783323"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783323; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783323]").text(description); $(".js-view-count[data-work-id=2783323]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783323; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783323']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "00cffd2ba2cea28abf544d3d4e7c316e" } } $('.js-work-strip[data-work-id=2783323]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783323,"title":"Emergence of behavioral primitives in self-organizing control and composition of behavior for autonomous robots","internal_url":"https://www.academia.edu/2783323/Emergence_of_behavioral_primitives_in_self_organizing_control_and_composition_of_behavior_for_autonomous_robots","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. 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We find this result to be inconsistent with probability summation models, and account for it by a model of'physiological'integration that is based on excitatory lateral interactions in the visual cortex.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="884a492259196f38bbcf8f2e30fdc866" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737085,"asset_id":2783320,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737085/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783320"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783320"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783320; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783320]").text(description); $(".js-view-count[data-work-id=2783320]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783320; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783320']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "884a492259196f38bbcf8f2e30fdc866" } } $('.js-work-strip[data-work-id=2783320]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783320,"title":"Mechanisms for spatial integration in visual detection: a model based on lateral interactions","internal_url":"https://www.academia.edu/2783320/Mechanisms_for_spatial_integration_in_visual_detection_a_model_based_on_lateral_interactions","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. 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In neural systems, the critical state is believed to optimize memory capacity, sensitivity to stimuli and information transmission. Critical avalanches were found in cortical cultures and slices [1] and in the motor cortex of awake monkeys [2]. Computational models of SOC often include an explicit regulatory mechanism that guides the state of the network toward criticality.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8a3d0e201eb9b5a36ec88020338f33cd" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737087,"asset_id":2783319,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737087/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783319"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783319"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783319; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783319]").text(description); $(".js-view-count[data-work-id=2783319]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783319; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783319']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8a3d0e201eb9b5a36ec88020338f33cd" } } $('.js-work-strip[data-work-id=2783319]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783319,"title":"Neural dynamics and network topology interact to form critical avalanches","internal_url":"https://www.academia.edu/2783319/Neural_dynamics_and_network_topology_interact_to_form_critical_avalanches","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737087,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"1471-2202-12-s1-p118.pdf","download_url":"https://www.academia.edu/attachments/30737087/download_file","bulk_download_file_name":"Neural_dynamics_and_network_topology_int.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737087/1471-2202-12-s1-p118-libre.pdf?1391780947=\u0026response-content-disposition=attachment%3B+filename%3DNeural_dynamics_and_network_topology_int.pdf\u0026Expires=1740602063\u0026Signature=TIS-2yhcsfy88NVYPagXmwAmDAOkpyNz9O3bO5c4KU2GnvzPuUS2nL5L33jnDoK3jJ6XxJI7PJljL3I7xudTEUlg0WptaPFDnNGx6vpHFDkUnfCAwu2Hcp5~ottodAYQHIw~teLjYAPQjKYPXgbmFlPV8nZs-jyrSsbmTIUcmT~L1R9zEsA7iBYXvKuSkQYOs0q~HrMRnJd7Hd4LYlTJG7g5aDPqq0lAP4Uw9GqfGyZhKBygko4-q5xKawWueUUWGzzesb7XKEPyQB8Zg9PmhvWCkHOYp4MTOnavYABoLJexR9ejRcEQhDut4hjHkMVbn1ZS2VBHHD2JqU8DSFRCaQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783318"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/2783318/Compositionality_of_arm_movements_can_be_realized_by_propagating_synchrony"><img alt="Research paper thumbnail of Compositionality of arm movements can be realized by propagating synchrony" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/2783318/Compositionality_of_arm_movements_can_be_realized_by_propagating_synchrony">Compositionality of arm movements can be realized by propagating synchrony</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract We present a biologically plausible spiking neuronal network model of free monkey scribb...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract We present a biologically plausible spiking neuronal network model of free monkey scribbling that reproduces experimental findings on cortical activity and the properties of the scribbling trajectory. The model is based on the idea that synfire chains can encode movement primitives. Here, we map the propagation of activity in a chain to a linearly evolving preferred velocity, which results in parabolic segments that fulfill the two-thirds power law.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783318"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783318"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783318; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783318]").text(description); $(".js-view-count[data-work-id=2783318]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783318; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783318']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=2783318]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783318,"title":"Compositionality of arm movements can be realized by propagating synchrony","internal_url":"https://www.academia.edu/2783318/Compositionality_of_arm_movements_can_be_realized_by_propagating_synchrony","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783317"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783317/Building_nonlinear_data_models_with_self_organizing_maps"><img alt="Research paper thumbnail of Building nonlinear data models with self-organizing maps" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783317/Building_nonlinear_data_models_with_self_organizing_maps">Building nonlinear data models with self-organizing maps</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We study the extraction of nonlinear data models in high dimensional spaces with modified self-or...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We study the extraction of nonlinear data models in high dimensional spaces with modified self-organizing maps. Our algorithm maps lower dimensional lattice into a high dimensional space without topology violations by tuning the neighborhood widths locally. The approach is based on a new principle exploiting the specific dynamical properties of the first order phase transition induced by the noise of the data. The performance of the algorithm is demonstrated for one-and two-dimensional principal manifolds and for sparse data sets.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a44c7fac975ef2f681fa247608787182" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737088,"asset_id":2783317,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737088/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783317"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783317"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783317; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783317]").text(description); $(".js-view-count[data-work-id=2783317]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783317; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783317']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a44c7fac975ef2f681fa247608787182" } } $('.js-work-strip[data-work-id=2783317]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783317,"title":"Building nonlinear data models with self-organizing maps","internal_url":"https://www.academia.edu/2783317/Building_nonlinear_data_models_with_self_organizing_maps","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737088,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"10.1.1.9.5170.pdf","download_url":"https://www.academia.edu/attachments/30737088/download_file","bulk_download_file_name":"Building_nonlinear_data_models_with_self.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737088/10.1.1.9.5170-libre.pdf?1391833387=\u0026response-content-disposition=attachment%3B+filename%3DBuilding_nonlinear_data_models_with_self.pdf\u0026Expires=1740602063\u0026Signature=CZgg4NdRkjmPVkS1HKA5pCCBg4gyqWIOc6~gh4Zxu7IH7TSyp-PnfzvO1c~k9KtziIJqYR0yTxOwtIh8LZf7HUADuuRsHwWuZl~Nvg8efIhJUtS5WyvkqryW9-NwjWwOKrP5nOOZpIOWrdX7aqD3CHZolSUBKaf-2-ndMX9nxoZO~3O09gJVGSNRgaSXtbLZKgxN7nHjBKJWqHBZh8lJRe46aJRr3bpvDipXTfZsEqv2Ib5UHlJNgHQ7FTXnGpaUOnF~5uNgKkFb5l561A8kEwgl9AhJAToLZJyPVNgsM36J6OIbJH4FETVkr8lj8sUN3xRwCA01-MrxoA0sMi~Aeg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783316"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783316/Gain_based_exploration_From_multi_armed_bandits_to_partially_observable_environments"><img alt="Research paper thumbnail of Gain-based exploration: From multi-armed bandits to partially observable environments" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783316/Gain_based_exploration_From_multi_armed_bandits_to_partially_observable_environments">Gain-based exploration: From multi-armed bandits to partially observable environments</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract We introduce gain-based policies for exploration in active learning problems. For explor...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract We introduce gain-based policies for exploration in active learning problems. For exploration in multi-armed bandits with the knowledge of reward variances, an ideal gain-maximization exploration policy is described in a unified framework which also includes error-based and counter-based exploration.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9cc6208577a793d3be5a95ee6194f36d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737089,"asset_id":2783316,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737089/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783316"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783316"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783316; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783316]").text(description); $(".js-view-count[data-work-id=2783316]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783316; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783316']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9cc6208577a793d3be5a95ee6194f36d" } } $('.js-work-strip[data-work-id=2783316]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783316,"title":"Gain-based exploration: From multi-armed bandits to partially observable environments","internal_url":"https://www.academia.edu/2783316/Gain_based_exploration_From_multi_armed_bandits_to_partially_observable_environments","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737089,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"Si_2007b.pdf","download_url":"https://www.academia.edu/attachments/30737089/download_file","bulk_download_file_name":"Gain_based_exploration_From_multi_armed.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737089/Si_2007b-libre.pdf?1391855039=\u0026response-content-disposition=attachment%3B+filename%3DGain_based_exploration_From_multi_armed.pdf\u0026Expires=1740602063\u0026Signature=aI5AbQqWLYo-p4vTUCak4tHKt72aZedPBi6mcxvrcNlfcUuoM0CAhg43hGRzcbLVJPTxGj8bJy6s~lllaugWArNUz4tYiIS0ce4n4i1y7PBRbSpAZGiPrPH1fdXyxGdZ60gtHtRsIRElRAR9mePsarBX9sdGdG6yoQNdJBVRWJid3fdo1gA9ARNfxE0Xlbf3RFDUknBxpn5EfgCp6h3q72s9IXAfNI0XSavIO6RLnZrnXoVW-XDiVD01w4jdlku2PJiX53JW3tRaR9aJnMTQOLQjurGD8zIJxLf5glzoYL7okXHJydw5sYtMEzuiAfJasgAz79V9DzZzz39aBM7WDg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783315"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783315/Symmetries_non_Euclidean_metrics_and_patterns_in_a_Swift_Hohenberg_model_of_the_visual_cortex"><img alt="Research paper thumbnail of Symmetries, non-Euclidean metrics, and patterns in a Swift鈥揌ohenberg model of the visual cortex" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783315/Symmetries_non_Euclidean_metrics_and_patterns_in_a_Swift_Hohenberg_model_of_the_visual_cortex">Symmetries, non-Euclidean metrics, and patterns in a Swift鈥揌ohenberg model of the visual cortex</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract The aim of this work is to investigate the effect of the shift-twist symmetry on pattern...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract The aim of this work is to investigate the effect of the shift-twist symmetry on pattern formation processes in the visual cortex. First, we describe a generic set of Riemannian metrics of the feature space of orientation preference that obeys properties of the shift-twist, translation, and reflection symmetries. Second, these metrics are embedded in a modified Swift鈥揌ohenberg model. As a result we get a pattern formation process that resembles the pattern formation process in the visual cortex.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f8f31fc30529bc5efe9a21d8aedefdaa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737090,"asset_id":2783315,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737090/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783315"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783315"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783315; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783315]").text(description); $(".js-view-count[data-work-id=2783315]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783315; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783315']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f8f31fc30529bc5efe9a21d8aedefdaa" } } $('.js-work-strip[data-work-id=2783315]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783315,"title":"Symmetries, non-Euclidean metrics, and patterns in a Swift鈥揌ohenberg model of the visual cortex","internal_url":"https://www.academia.edu/2783315/Symmetries_non_Euclidean_metrics_and_patterns_in_a_Swift_Hohenberg_model_of_the_visual_cortex","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737090,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"herrmanncybernetics.pdf","download_url":"https://www.academia.edu/attachments/30737090/download_file","bulk_download_file_name":"Symmetries_non_Euclidean_metrics_and_pat.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737090/herrmanncybernetics-libre.pdf?1392055182=\u0026response-content-disposition=attachment%3B+filename%3DSymmetries_non_Euclidean_metrics_and_pat.pdf\u0026Expires=1740602063\u0026Signature=Tm0rbHJF-jZar-LxjNA8eWwg2n7JEtRKiwiRrNT2rXmbToVDPCNi7beGEqVfGRgHvrblBvY53LmkX3y4FfBZQahH8jsUwp8-fBIlC64HCV922GEqX7bV8G9tPuSse7sTwFGywVNaLDY1ZWaEixotKREK~ScWYlqwugw7MRS8E~nqTNX1n8IsLdCkUwhoFCkUyAHnqxHM-DBgiA9dngfKwLaMdVUYvIVh70PLffnhibzNpg12jlqPhxg2HvL8F5~Z83oNB8cciO8gj4b7LL85QfTSrIAPvg7BbmJbFxOyDcQU0U7tREPezzY9T-B95if2zTFfhcDq2~d04OasQkq6FQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783314"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783314/Playing_robots_Self_organisation_of_behaviour_in_a_dynamical_system_perspective"><img alt="Research paper thumbnail of Playing robots: Self-organisation of behaviour in a dynamical system perspective" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783314/Playing_robots_Self_organisation_of_behaviour_in_a_dynamical_system_perspective">Playing robots: Self-organisation of behaviour in a dynamical system perspective</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Summary Mobile robots face a complex environment which cannot be expected to be fully predicable....</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Summary Mobile robots face a complex environment which cannot be expected to be fully predicable. Selfdetermined exploration is a viable approach to achieve an accumulation of world knowledge by the robot. The tutorial explains how the homeokinetic principle gives rise to exploratory robot controllers that produce play-like behaviour by maximising information gain.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7038fa28c4eefeb8c76b9a4ffc3b7992" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737091,"asset_id":2783314,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737091/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783314"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783314"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783314; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783314]").text(description); $(".js-view-count[data-work-id=2783314]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783314; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783314']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7038fa28c4eefeb8c76b9a4ffc3b7992" } } $('.js-work-strip[data-work-id=2783314]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783314,"title":"Playing robots: Self-organisation of behaviour in a dynamical system perspective","internal_url":"https://www.academia.edu/2783314/Playing_robots_Self_organisation_of_behaviour_in_a_dynamical_system_perspective","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737091,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"hci2010tutorial.pdf","download_url":"https://www.academia.edu/attachments/30737091/download_file","bulk_download_file_name":"Playing_robots_Self_organisation_of_beha.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737091/hci2010tutorial-libre.pdf?1392114611=\u0026response-content-disposition=attachment%3B+filename%3DPlaying_robots_Self_organisation_of_beha.pdf\u0026Expires=1740602063\u0026Signature=S0tru5g7R478NfZDdh-WfMFhOJaKb1nmFkdrflTa4wVs6TnIPdX9qBz9wGBJ8EUzZ49B5ktH8Xf6p7VnZ6XW1A~~R-xLDtaNEcxpiiuXGokvwOeODd0NLFEyNHuci6GnlUqFTzf1SkufsVMnFnDCE3frvYGI3WRbUezCPGgLeuehOJ-u5Zyfd1yunvyypTTUzR3q8apxJv203yDJd1C5LA9r9TTzO98oWCSwXZhT3JZ71JhXPIRRUcduYZ7ABcPbE4V1bJ3eOj~uOYkPJu02bqFQeU~ErT3WaP2h-jsv8MWBGoMLQ7uXb6E--~~G9dY6XFFWtr8aTZtvjVVF8Sus5w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783313"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783313/Discrete_Breathers_in_Neural_Networks"><img alt="Research paper thumbnail of Discrete Breathers in Neural Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783313/Discrete_Breathers_in_Neural_Networks">Discrete Breathers in Neural Networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Localization in non-linear lattices of excitable elements is present in discrete breathe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Localization in non-linear lattices of excitable elements is present in discrete breathers [1], and forms an interesting counterpart to localization of activity in neural systems [4]. We study the behavior of breatherlike excitations in a system of locally interacting integrate-and-fire neurons. Both numerical and analytical results justify the notion of a neural breather, which may form an element of working memory and attention.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a441a5eb3a2861003823e3667215bd76" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737092,"asset_id":2783313,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737092/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783313"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783313"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783313; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783313]").text(description); $(".js-view-count[data-work-id=2783313]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783313; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783313']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a441a5eb3a2861003823e3667215bd76" } } $('.js-work-strip[data-work-id=2783313]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783313,"title":"Discrete Breathers in Neural Networks","internal_url":"https://www.academia.edu/2783313/Discrete_Breathers_in_Neural_Networks","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737092,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"A0dpg05.pdf","download_url":"https://www.academia.edu/attachments/30737092/download_file","bulk_download_file_name":"Discrete_Breathers_in_Neural_Networks.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737092/A0dpg05-libre.pdf?1392083352=\u0026response-content-disposition=attachment%3B+filename%3DDiscrete_Breathers_in_Neural_Networks.pdf\u0026Expires=1740602063\u0026Signature=gSAukHqMBBmk-jsZZeLqb2vNBLgcdhrgV2eU18Eu2IDYMOWGaSvbZts92g0VQassUyH39LmItqNiyISsJaBseDOyRgSnj83HARqRdoyyQSgUR6hxhp3kXDXFqCxi3EWdO0HTP0OFRRvoTK9eyfMls2r0RpNpXaqjdxCusj0AxIkcnfWhdYKNmFVPJBNc8qYySBtgLJ6dJEpIxq6mGDhLC5njPAD-ANJH99aW4GWAzMUOsNGHiDJfjNOs1~V5OCxSDjREwpSATmaQvrpA7zj7D4j08OKGP1MWjbmggU~pkBTLAFnkcoYXQfmJMHdgXy~4zAcRjwQO3hYvEYsUBqRyrA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783312"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783312/Localized_Solutions_in_a_Simple_Neural_Field_Model"><img alt="Research paper thumbnail of Localized Solutions in a Simple Neural Field Model" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783312/Localized_Solutions_in_a_Simple_Neural_Field_Model">Localized Solutions in a Simple Neural Field Model</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract We investigate analytically properties like stability and existence of solutions of the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract We investigate analytically properties like stability and existence of solutions of the two dimensional neural field equation as proposed by Amari (1977) in [1] as a model of macroscopic activation dynamics in neural tissue. While the one dimensional case has been treated comprehensively, for the two dimensional case only the existence of circular solutions was shown, and stability was as well only considered for radially symmetric perturbations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="73c871400f9c88600b80d2b32bc9994e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737093,"asset_id":2783312,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737093/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783312"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783312"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783312; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783312]").text(description); $(".js-view-count[data-work-id=2783312]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783312; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783312']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "73c871400f9c88600b80d2b32bc9994e" } } $('.js-work-strip[data-work-id=2783312]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783312,"title":"Localized Solutions in a Simple Neural Field Model","internal_url":"https://www.academia.edu/2783312/Localized_Solutions_in_a_Simple_Neural_Field_Model","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737093,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"A0cns04.pdf","download_url":"https://www.academia.edu/attachments/30737093/download_file","bulk_download_file_name":"Localized_Solutions_in_a_Simple_Neural_F.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737093/A0cns04-libre.pdf?1392886301=\u0026response-content-disposition=attachment%3B+filename%3DLocalized_Solutions_in_a_Simple_Neural_F.pdf\u0026Expires=1740602063\u0026Signature=TKCWd8vqqQQp6eOG-5TKwt-w-f-tDlsczAeIB9oVAybpHa3LbPTupi0RJzLYu0Bwfmn0hoWfiAxGF79LKTlJaxGB7JKgn-PqvSHy6-QWy0sWC0wGDsuDQtYJX7fHf7pf2dkIsxPhkZT3pg7z1b3I-eSB2oVc86kgiB~NCwcz4AKLezNsz-aOJHDx4ePkLfQnkgmMMHvl7gXEuuYJLS4gAY~O640Qy32lHyEWgXPkkHCbbAakIkucZDJ-wn5m0YrIO6wYW9nyv3IPmofDuzYhkuxqaaXAeNFKn~r0mkR6xtbsnNX6LiR3QTR0OXIWk16NXJKgzgmuCmZTmqWcR6BGyg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783311"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/2783311/The_General_Model_for_Negative_Priming"><img alt="Research paper thumbnail of The General Model for Negative Priming" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/2783311/The_General_Model_for_Negative_Priming">The General Model for Negative Priming</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Negative priming is characterized by longer reaction times when responding to stimuli wh...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Negative priming is characterized by longer reaction times when responding to stimuli which have been actively ignored recently. A central problem of the interpretation of the NP effect is the lack of agreement about the underlying mechanisms. Over the past 20 years, various theoretical accounts have been developed to explain NP. However, empirical evidence does not clearly favour one theory over the others.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783311"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783311"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783311; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783311]").text(description); $(".js-view-count[data-work-id=2783311]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783311; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783311']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=2783311]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783311,"title":"The General Model for Negative Priming","internal_url":"https://www.academia.edu/2783311/The_General_Model_for_Negative_Priming","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783310"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783310/Switching_to_criticality_by_synchronized_input"><img alt="Research paper thumbnail of Switching to criticality by synchronized input" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783310/Switching_to_criticality_by_synchronized_input">Switching to criticality by synchronized input</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It was previously shown that an extended critical interval can be obtained in a neural network by...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It was previously shown that an extended critical interval can be obtained in a neural network by incorporation of depressive synapses [2]. In the present study we scrutinize a more realistic dynamics for the synaptic interactions that can be considered as the state-of-the-art in computational modeling of synaptic interaction (Figure 1)[2].</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e8be925edd427283ea54cd4f8db043e4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737094,"asset_id":2783310,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737094/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783310"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783310"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783310; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783310]").text(description); $(".js-view-count[data-work-id=2783310]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783310; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783310']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e8be925edd427283ea54cd4f8db043e4" } } $('.js-work-strip[data-work-id=2783310]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783310,"title":"Switching to criticality by synchronized input","internal_url":"https://www.academia.edu/2783310/Switching_to_criticality_by_synchronized_input","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737094,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"1471-2202-10-S1-P155.pdf","download_url":"https://www.academia.edu/attachments/30737094/download_file","bulk_download_file_name":"Switching_to_criticality_by_synchronized.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737094/1471-2202-10-S1-P155-libre.pdf?1391028630=\u0026response-content-disposition=attachment%3B+filename%3DSwitching_to_criticality_by_synchronized.pdf\u0026Expires=1740602063\u0026Signature=QYnru1MaDt~eiZR1dQwlT-LXwmF2hbDc9Lnpa0LvS1w8txWLhPjy2NbnvycOZu3BVFzM4~x9cQta3GQSMllBfYlGhsWeYmMlpbyv52su4BCMNJvumMQ3XyZ7Dmg8n9we1Asiny21jcB0ugBI0~3aImegvAfexJ2c6aUVdTx9q75PL1YFC25b8T8op7WDTTr-Zp1Oqrj6UVse5Z8kqPjH78kYGXZJN7gHEIxejkwLPQlGRw-KJCnX693~c90O6BG03UOf2y~ZO3eZ-wGsA7ZLIc7qTHvcO3iA6KJudhPuzWu6jgNpdmVyRtbOfENxydf2rfbrkQz74DhuKQYwSdrttg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783309"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783309/A_sensor_based_learning_algorithm_for_the_self_organization_of_robot_behavior"><img alt="Research paper thumbnail of A sensor-based learning algorithm for the self-organization of robot behavior" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783309/A_sensor_based_learning_algorithm_for_the_self_organization_of_robot_behavior">A sensor-based learning algorithm for the self-organization of robot behavior</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract: Ideally, sensory information forms the only source of information to a robot. We consid...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract: Ideally, sensory information forms the only source of information to a robot. We consider an algorithm for the self-organization of a controller. At short time scales the controller is merely reactive but the parameter dynamics and the acquisition of knowledge by an internal model lead to seemingly purposeful behavior on longer time scales. As a paradigmatic example, we study the simulation of an underactuated snake-like robot.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="24b3fd9f5b1fafe22621e41f840a57c0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737095,"asset_id":2783309,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737095/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783309"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783309"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783309; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783309]").text(description); $(".js-view-count[data-work-id=2783309]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783309; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783309']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "24b3fd9f5b1fafe22621e41f840a57c0" } } $('.js-work-strip[data-work-id=2783309]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783309,"title":"A sensor-based learning algorithm for the self-organization of robot behavior","internal_url":"https://www.academia.edu/2783309/A_sensor_based_learning_algorithm_for_the_self_organization_of_robot_behavior","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737095,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"algorithms-02-00398.pdf","download_url":"https://www.academia.edu/attachments/30737095/download_file","bulk_download_file_name":"A_sensor_based_learning_algorithm_for_th.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737095/algorithms-02-00398-libre.pdf?1392043260=\u0026response-content-disposition=attachment%3B+filename%3DA_sensor_based_learning_algorithm_for_th.pdf\u0026Expires=1740602063\u0026Signature=Gv3H4a00zOSQ9URYOQSb5~QoyidnOHW9Budc2Tyq8jJMhvOUk71lT7f4I64N5XQ1HdLzH1bwBwnIH-HQTLym5xOfq3LGHUHL0mwV~fxDCstNu79C-cq4TXMKPPtAE3qjIMFGtHqc2hL~0Hc1AxrY-ojEUsW0jGIdTaCRRFE~jHfUZ7Z2Yjmh3FPwHqVWWgJiflOCTXgWC6Cu2WxrGEmLP4OfZDcAxv~G4YxPXMzsF5bZQKYccRAnfI8Hg3Efx2lu6YZeqCHYvN9LNizv098LEEwAcT7sc7uXSe-9BOOrEjeO9tL6Iv9Vl9fbfRAdQdROhVdC8h9agHrT7ICCRTdu-A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783308"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783308/An_algorithm_for_generalized_principal_curves_with_adaptive_topology_in_complex_data_sets"><img alt="Research paper thumbnail of An algorithm for generalized principal curves with adaptive topology in complex data sets" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783308/An_algorithm_for_generalized_principal_curves_with_adaptive_topology_in_complex_data_sets">An algorithm for generalized principal curves with adaptive topology in complex data sets</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract. Generalized principal curves are capable of representing complex data structures as the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract. Generalized principal curves are capable of representing complex data structures as they may have branching points or may consist of disconnected parts. For their construction using an unsupervised learning algorithm the templates need to be structurally adaptive. The present algorithm meets this goal by a combination of a competitive Hebbian learning scheme and a self-organizing map algorithm.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9869e73eac9dd35e419319fb1b4aa305" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737097,"asset_id":2783308,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737097/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783308"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783308"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783308; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783308]").text(description); $(".js-view-count[data-work-id=2783308]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783308; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783308']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9869e73eac9dd35e419319fb1b4aa305" } } $('.js-work-strip[data-work-id=2783308]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783308,"title":"An algorithm for generalized principal curves with adaptive topology in complex data sets","internal_url":"https://www.academia.edu/2783308/An_algorithm_for_generalized_principal_curves_with_adaptive_topology_in_complex_data_sets","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737097,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"10.1.1.224.5762.pdf","download_url":"https://www.academia.edu/attachments/30737097/download_file","bulk_download_file_name":"An_algorithm_for_generalized_principal_c.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737097/10.1.1.224.5762-libre.pdf?1391870033=\u0026response-content-disposition=attachment%3B+filename%3DAn_algorithm_for_generalized_principal_c.pdf\u0026Expires=1740602063\u0026Signature=JhrdaEubecGmIN4iHphQ~MDEqA2Qa~hTK4Z2vo2LIjYlzR3J7Xmd4gYNcIHYrg~oIFWTFweysh5yQDjRBAq1q1ktGXU2yTTmnzNGbAn~fswe6cZ3VE5bWYE~ezkR9yssJwDSPJ-SWI4y8mHTGTBlQ4A37xUYCoJSrjGNIB6IbsgALI3pK5Pj0Ga2QqRQwd-qRCcicUsmzmZ~j3WJK9SClaPRr39fvYODCZmcBfCA2EGKHIS2W562lmV9hLEAfCu146C8Uc5-Yjn37pRFKIygVCmRjcYvcnoee4AwPYZEvSZ4g7-oOmLMmfLYYIIUVObweNviO1qPKZyDfXsLR0iEcg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="399354" id="papers"><div class="js-work-strip profile--work_container" data-work-id="20282108"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/20282108/Structured_control_from_self_organizing_arm_movements"><img alt="Research paper thumbnail of Structured control from self-organizing arm movements" class="work-thumbnail" src="https://attachments.academia-assets.com/41344132/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/20282108/Structured_control_from_self_organizing_arm_movements">Structured control from self-organizing arm movements</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/GeorgMartius">Georg Martius</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://edinburgh.academia.edu/DrMichaelHerrmann">J. Michael Herrmann</a></span></div><div class="wp-workCard_item"><span>BMC Neuroscience</span><span>, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8fb45f3323b09959609aabe8847c2828" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41344132,"asset_id":20282108,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41344132/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="20282108"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="20282108"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20282108; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20282108]").text(description); $(".js-view-count[data-work-id=20282108]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20282108; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='20282108']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783326"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783326/A_Computational_Account_of_the_Negative_Priming_Effect"><img alt="Research paper thumbnail of A Computational Account of the Negative Priming Effect" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783326/A_Computational_Account_of_the_Negative_Priming_Effect">A Computational Account of the Negative Priming Effect</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We will present the implementation of a recent explanation to a prominent effect in psychological...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We will present the implementation of a recent explanation to a prominent effect in psychological problems, negative priming. A typical setup to determine priming effects is the identity priming paradigm as it is shown in fig. 1. A person fixates a cross on a screen and is presented two stimuli at a time. Some feature (here: color) determines the discrimination between target and distractor. The green stimulus is the target and the red one the distractor.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ae052d1982f9dfdb60692fc02e6a6245" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737146,"asset_id":2783326,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737146/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783326"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783326"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783326; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783326]").text(description); $(".js-view-count[data-work-id=2783326]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783326; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783326']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ae052d1982f9dfdb60692fc02e6a6245" } } $('.js-work-strip[data-work-id=2783326]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783326,"title":"A Computational Account of the Negative Priming Effect","internal_url":"https://www.academia.edu/2783326/A_Computational_Account_of_the_Negative_Priming_Effect","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. 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This is not only because systems become more lifelike, but also since self-organisation may help in reducing the design efforts in creating complex behaviour systems. We consider agents under the close sensorimotor coupling paradigm with a certain cognitive ability realised by an internal forward model.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783325"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783325"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783325; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783325]").text(description); $(".js-view-count[data-work-id=2783325]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783325; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783325']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=2783325]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783325,"title":"Self-exploration in a DS Approach to Early Robot Development","internal_url":"https://www.academia.edu/2783325/Self_exploration_in_a_DS_Approach_to_Early_Robot_Development","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783324"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783324/A_feature_binding_model_with_localized_excitations"><img alt="Research paper thumbnail of A feature-binding model with localized excitations" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783324/A_feature_binding_model_with_localized_excitations">A feature-binding model with localized excitations</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We study a model of feature binding in prefrontal cortex which defers specific perceptual informa...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We study a model of feature binding in prefrontal cortex which defers specific perceptual information to lower areas and merely maintains the identity of the combination. The model consists of three layers of pulse-coupled leaky integrate-and-fire neurons. Features are encoded by the location of sustained activity in the subordinate layers. The feature layers are excitatorily coupled to a superordinate layer that represents combinations of features by means of an oscillatory dynamics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6954ecfaa9a0d5c6fdd2a17554d4c914" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737157,"asset_id":2783324,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737157/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783324"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783324"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783324; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783324]").text(description); $(".js-view-count[data-work-id=2783324]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783324; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783324']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6954ecfaa9a0d5c6fdd2a17554d4c914" } } $('.js-work-strip[data-work-id=2783324]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783324,"title":"A feature-binding model with localized excitations","internal_url":"https://www.academia.edu/2783324/A_feature_binding_model_with_localized_excitations","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737157,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"a_feature_binding_model_with_localized_excitation.pdf","download_url":"https://www.academia.edu/attachments/30737157/download_file","bulk_download_file_name":"A_feature_binding_model_with_localized_e.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737157/a_feature_binding_model_with_localized_excitation-libre.pdf?1391817312=\u0026response-content-disposition=attachment%3B+filename%3DA_feature_binding_model_with_localized_e.pdf\u0026Expires=1740602063\u0026Signature=fEWMdr2fiyHEseT8435LrDkJlFShkqWJwcaajL3JK4gTzIxR6mQqHOBznthN6dPdPgXxqCLD7v9-XsRxfohgPfjAd6-fEmJ7R7vnzyv6fODS4-5QPX56gXj8zprs4wTr-58-JL3tNnbrF-zoCAsMEfSD5KCvGKOlshU3b6xNSpDdNoeX7icmsDUk~KYXRtRpLp4PHtZ5xgr~QX0BREnjxH3VVAU0APTcvYF4WhJGCYrv0m8n7TajFjtRHQFtDrxv5~f2JHsURdd7yeulbIJ8iqPyAwPmhAZ9Iy0TG5Kw1Z~W4YpaleTKToUkK3aNkkC2BAI1SitDUIULbwoCooc47g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783323"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783323/Emergence_of_behavioral_primitives_in_self_organizing_control_and_composition_of_behavior_for_autonomous_robots"><img alt="Research paper thumbnail of Emergence of behavioral primitives in self-organizing control and composition of behavior for autonomous robots" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783323/Emergence_of_behavioral_primitives_in_self_organizing_control_and_composition_of_behavior_for_autonomous_robots">Emergence of behavioral primitives in self-organizing control and composition of behavior for autonomous robots</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background Autonomous robots as well as animals process sensory information for the purpose of ge...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Background Autonomous robots as well as animals process sensory information for the purpose of generating behaviors that are adapted to their respective environments. This includes the selection of behaviorally relevant perceptual features, the adaptation of control mechanisms and the storage and the recall of behavioral episodes for planning and execution.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="00cffd2ba2cea28abf544d3d4e7c316e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737106,"asset_id":2783323,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737106/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783323"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783323"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783323; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783323]").text(description); $(".js-view-count[data-work-id=2783323]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783323; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783323']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "00cffd2ba2cea28abf544d3d4e7c316e" } } $('.js-work-strip[data-work-id=2783323]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783323,"title":"Emergence of behavioral primitives in self-organizing control and composition of behavior for autonomous robots","internal_url":"https://www.academia.edu/2783323/Emergence_of_behavioral_primitives_in_self_organizing_control_and_composition_of_behavior_for_autonomous_robots","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. 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We find this result to be inconsistent with probability summation models, and account for it by a model of'physiological'integration that is based on excitatory lateral interactions in the visual cortex.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="884a492259196f38bbcf8f2e30fdc866" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737085,"asset_id":2783320,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737085/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783320"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783320"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783320; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783320]").text(description); $(".js-view-count[data-work-id=2783320]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783320; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783320']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "884a492259196f38bbcf8f2e30fdc866" } } $('.js-work-strip[data-work-id=2783320]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783320,"title":"Mechanisms for spatial integration in visual detection: a model based on lateral interactions","internal_url":"https://www.academia.edu/2783320/Mechanisms_for_spatial_integration_in_visual_detection_a_model_based_on_lateral_interactions","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. 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In neural systems, the critical state is believed to optimize memory capacity, sensitivity to stimuli and information transmission. Critical avalanches were found in cortical cultures and slices [1] and in the motor cortex of awake monkeys [2]. Computational models of SOC often include an explicit regulatory mechanism that guides the state of the network toward criticality.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8a3d0e201eb9b5a36ec88020338f33cd" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737087,"asset_id":2783319,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737087/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783319"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783319"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783319; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783319]").text(description); $(".js-view-count[data-work-id=2783319]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783319; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783319']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8a3d0e201eb9b5a36ec88020338f33cd" } } $('.js-work-strip[data-work-id=2783319]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783319,"title":"Neural dynamics and network topology interact to form critical avalanches","internal_url":"https://www.academia.edu/2783319/Neural_dynamics_and_network_topology_interact_to_form_critical_avalanches","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737087,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"1471-2202-12-s1-p118.pdf","download_url":"https://www.academia.edu/attachments/30737087/download_file","bulk_download_file_name":"Neural_dynamics_and_network_topology_int.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737087/1471-2202-12-s1-p118-libre.pdf?1391780947=\u0026response-content-disposition=attachment%3B+filename%3DNeural_dynamics_and_network_topology_int.pdf\u0026Expires=1740602063\u0026Signature=TIS-2yhcsfy88NVYPagXmwAmDAOkpyNz9O3bO5c4KU2GnvzPuUS2nL5L33jnDoK3jJ6XxJI7PJljL3I7xudTEUlg0WptaPFDnNGx6vpHFDkUnfCAwu2Hcp5~ottodAYQHIw~teLjYAPQjKYPXgbmFlPV8nZs-jyrSsbmTIUcmT~L1R9zEsA7iBYXvKuSkQYOs0q~HrMRnJd7Hd4LYlTJG7g5aDPqq0lAP4Uw9GqfGyZhKBygko4-q5xKawWueUUWGzzesb7XKEPyQB8Zg9PmhvWCkHOYp4MTOnavYABoLJexR9ejRcEQhDut4hjHkMVbn1ZS2VBHHD2JqU8DSFRCaQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783318"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/2783318/Compositionality_of_arm_movements_can_be_realized_by_propagating_synchrony"><img alt="Research paper thumbnail of Compositionality of arm movements can be realized by propagating synchrony" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/2783318/Compositionality_of_arm_movements_can_be_realized_by_propagating_synchrony">Compositionality of arm movements can be realized by propagating synchrony</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract We present a biologically plausible spiking neuronal network model of free monkey scribb...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract We present a biologically plausible spiking neuronal network model of free monkey scribbling that reproduces experimental findings on cortical activity and the properties of the scribbling trajectory. The model is based on the idea that synfire chains can encode movement primitives. Here, we map the propagation of activity in a chain to a linearly evolving preferred velocity, which results in parabolic segments that fulfill the two-thirds power law.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783318"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783318"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783318; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783318]").text(description); $(".js-view-count[data-work-id=2783318]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783318; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783318']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=2783318]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783318,"title":"Compositionality of arm movements can be realized by propagating synchrony","internal_url":"https://www.academia.edu/2783318/Compositionality_of_arm_movements_can_be_realized_by_propagating_synchrony","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783317"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783317/Building_nonlinear_data_models_with_self_organizing_maps"><img alt="Research paper thumbnail of Building nonlinear data models with self-organizing maps" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783317/Building_nonlinear_data_models_with_self_organizing_maps">Building nonlinear data models with self-organizing maps</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We study the extraction of nonlinear data models in high dimensional spaces with modified self-or...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We study the extraction of nonlinear data models in high dimensional spaces with modified self-organizing maps. Our algorithm maps lower dimensional lattice into a high dimensional space without topology violations by tuning the neighborhood widths locally. The approach is based on a new principle exploiting the specific dynamical properties of the first order phase transition induced by the noise of the data. The performance of the algorithm is demonstrated for one-and two-dimensional principal manifolds and for sparse data sets.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a44c7fac975ef2f681fa247608787182" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737088,"asset_id":2783317,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737088/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783317"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783317"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783317; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783317]").text(description); $(".js-view-count[data-work-id=2783317]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783317; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783317']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a44c7fac975ef2f681fa247608787182" } } $('.js-work-strip[data-work-id=2783317]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783317,"title":"Building nonlinear data models with self-organizing maps","internal_url":"https://www.academia.edu/2783317/Building_nonlinear_data_models_with_self_organizing_maps","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737088,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"10.1.1.9.5170.pdf","download_url":"https://www.academia.edu/attachments/30737088/download_file","bulk_download_file_name":"Building_nonlinear_data_models_with_self.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737088/10.1.1.9.5170-libre.pdf?1391833387=\u0026response-content-disposition=attachment%3B+filename%3DBuilding_nonlinear_data_models_with_self.pdf\u0026Expires=1740602063\u0026Signature=CZgg4NdRkjmPVkS1HKA5pCCBg4gyqWIOc6~gh4Zxu7IH7TSyp-PnfzvO1c~k9KtziIJqYR0yTxOwtIh8LZf7HUADuuRsHwWuZl~Nvg8efIhJUtS5WyvkqryW9-NwjWwOKrP5nOOZpIOWrdX7aqD3CHZolSUBKaf-2-ndMX9nxoZO~3O09gJVGSNRgaSXtbLZKgxN7nHjBKJWqHBZh8lJRe46aJRr3bpvDipXTfZsEqv2Ib5UHlJNgHQ7FTXnGpaUOnF~5uNgKkFb5l561A8kEwgl9AhJAToLZJyPVNgsM36J6OIbJH4FETVkr8lj8sUN3xRwCA01-MrxoA0sMi~Aeg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783316"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783316/Gain_based_exploration_From_multi_armed_bandits_to_partially_observable_environments"><img alt="Research paper thumbnail of Gain-based exploration: From multi-armed bandits to partially observable environments" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783316/Gain_based_exploration_From_multi_armed_bandits_to_partially_observable_environments">Gain-based exploration: From multi-armed bandits to partially observable environments</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract We introduce gain-based policies for exploration in active learning problems. For explor...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract We introduce gain-based policies for exploration in active learning problems. For exploration in multi-armed bandits with the knowledge of reward variances, an ideal gain-maximization exploration policy is described in a unified framework which also includes error-based and counter-based exploration.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9cc6208577a793d3be5a95ee6194f36d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737089,"asset_id":2783316,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737089/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783316"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783316"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783316; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783316]").text(description); $(".js-view-count[data-work-id=2783316]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783316; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783316']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9cc6208577a793d3be5a95ee6194f36d" } } $('.js-work-strip[data-work-id=2783316]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783316,"title":"Gain-based exploration: From multi-armed bandits to partially observable environments","internal_url":"https://www.academia.edu/2783316/Gain_based_exploration_From_multi_armed_bandits_to_partially_observable_environments","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737089,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"Si_2007b.pdf","download_url":"https://www.academia.edu/attachments/30737089/download_file","bulk_download_file_name":"Gain_based_exploration_From_multi_armed.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737089/Si_2007b-libre.pdf?1391855039=\u0026response-content-disposition=attachment%3B+filename%3DGain_based_exploration_From_multi_armed.pdf\u0026Expires=1740602063\u0026Signature=aI5AbQqWLYo-p4vTUCak4tHKt72aZedPBi6mcxvrcNlfcUuoM0CAhg43hGRzcbLVJPTxGj8bJy6s~lllaugWArNUz4tYiIS0ce4n4i1y7PBRbSpAZGiPrPH1fdXyxGdZ60gtHtRsIRElRAR9mePsarBX9sdGdG6yoQNdJBVRWJid3fdo1gA9ARNfxE0Xlbf3RFDUknBxpn5EfgCp6h3q72s9IXAfNI0XSavIO6RLnZrnXoVW-XDiVD01w4jdlku2PJiX53JW3tRaR9aJnMTQOLQjurGD8zIJxLf5glzoYL7okXHJydw5sYtMEzuiAfJasgAz79V9DzZzz39aBM7WDg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783315"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783315/Symmetries_non_Euclidean_metrics_and_patterns_in_a_Swift_Hohenberg_model_of_the_visual_cortex"><img alt="Research paper thumbnail of Symmetries, non-Euclidean metrics, and patterns in a Swift鈥揌ohenberg model of the visual cortex" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783315/Symmetries_non_Euclidean_metrics_and_patterns_in_a_Swift_Hohenberg_model_of_the_visual_cortex">Symmetries, non-Euclidean metrics, and patterns in a Swift鈥揌ohenberg model of the visual cortex</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract The aim of this work is to investigate the effect of the shift-twist symmetry on pattern...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract The aim of this work is to investigate the effect of the shift-twist symmetry on pattern formation processes in the visual cortex. First, we describe a generic set of Riemannian metrics of the feature space of orientation preference that obeys properties of the shift-twist, translation, and reflection symmetries. Second, these metrics are embedded in a modified Swift鈥揌ohenberg model. As a result we get a pattern formation process that resembles the pattern formation process in the visual cortex.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f8f31fc30529bc5efe9a21d8aedefdaa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737090,"asset_id":2783315,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737090/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783315"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783315"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783315; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783315]").text(description); $(".js-view-count[data-work-id=2783315]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783315; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783315']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f8f31fc30529bc5efe9a21d8aedefdaa" } } $('.js-work-strip[data-work-id=2783315]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783315,"title":"Symmetries, non-Euclidean metrics, and patterns in a Swift鈥揌ohenberg model of the visual cortex","internal_url":"https://www.academia.edu/2783315/Symmetries_non_Euclidean_metrics_and_patterns_in_a_Swift_Hohenberg_model_of_the_visual_cortex","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737090,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"herrmanncybernetics.pdf","download_url":"https://www.academia.edu/attachments/30737090/download_file","bulk_download_file_name":"Symmetries_non_Euclidean_metrics_and_pat.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737090/herrmanncybernetics-libre.pdf?1392055182=\u0026response-content-disposition=attachment%3B+filename%3DSymmetries_non_Euclidean_metrics_and_pat.pdf\u0026Expires=1740602063\u0026Signature=Tm0rbHJF-jZar-LxjNA8eWwg2n7JEtRKiwiRrNT2rXmbToVDPCNi7beGEqVfGRgHvrblBvY53LmkX3y4FfBZQahH8jsUwp8-fBIlC64HCV922GEqX7bV8G9tPuSse7sTwFGywVNaLDY1ZWaEixotKREK~ScWYlqwugw7MRS8E~nqTNX1n8IsLdCkUwhoFCkUyAHnqxHM-DBgiA9dngfKwLaMdVUYvIVh70PLffnhibzNpg12jlqPhxg2HvL8F5~Z83oNB8cciO8gj4b7LL85QfTSrIAPvg7BbmJbFxOyDcQU0U7tREPezzY9T-B95if2zTFfhcDq2~d04OasQkq6FQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783314"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783314/Playing_robots_Self_organisation_of_behaviour_in_a_dynamical_system_perspective"><img alt="Research paper thumbnail of Playing robots: Self-organisation of behaviour in a dynamical system perspective" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783314/Playing_robots_Self_organisation_of_behaviour_in_a_dynamical_system_perspective">Playing robots: Self-organisation of behaviour in a dynamical system perspective</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Summary Mobile robots face a complex environment which cannot be expected to be fully predicable....</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Summary Mobile robots face a complex environment which cannot be expected to be fully predicable. Selfdetermined exploration is a viable approach to achieve an accumulation of world knowledge by the robot. The tutorial explains how the homeokinetic principle gives rise to exploratory robot controllers that produce play-like behaviour by maximising information gain.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7038fa28c4eefeb8c76b9a4ffc3b7992" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737091,"asset_id":2783314,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737091/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783314"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783314"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783314; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783314]").text(description); $(".js-view-count[data-work-id=2783314]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783314; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783314']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7038fa28c4eefeb8c76b9a4ffc3b7992" } } $('.js-work-strip[data-work-id=2783314]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783314,"title":"Playing robots: Self-organisation of behaviour in a dynamical system perspective","internal_url":"https://www.academia.edu/2783314/Playing_robots_Self_organisation_of_behaviour_in_a_dynamical_system_perspective","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737091,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"hci2010tutorial.pdf","download_url":"https://www.academia.edu/attachments/30737091/download_file","bulk_download_file_name":"Playing_robots_Self_organisation_of_beha.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737091/hci2010tutorial-libre.pdf?1392114611=\u0026response-content-disposition=attachment%3B+filename%3DPlaying_robots_Self_organisation_of_beha.pdf\u0026Expires=1740602063\u0026Signature=S0tru5g7R478NfZDdh-WfMFhOJaKb1nmFkdrflTa4wVs6TnIPdX9qBz9wGBJ8EUzZ49B5ktH8Xf6p7VnZ6XW1A~~R-xLDtaNEcxpiiuXGokvwOeODd0NLFEyNHuci6GnlUqFTzf1SkufsVMnFnDCE3frvYGI3WRbUezCPGgLeuehOJ-u5Zyfd1yunvyypTTUzR3q8apxJv203yDJd1C5LA9r9TTzO98oWCSwXZhT3JZ71JhXPIRRUcduYZ7ABcPbE4V1bJ3eOj~uOYkPJu02bqFQeU~ErT3WaP2h-jsv8MWBGoMLQ7uXb6E--~~G9dY6XFFWtr8aTZtvjVVF8Sus5w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783313"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783313/Discrete_Breathers_in_Neural_Networks"><img alt="Research paper thumbnail of Discrete Breathers in Neural Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783313/Discrete_Breathers_in_Neural_Networks">Discrete Breathers in Neural Networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Localization in non-linear lattices of excitable elements is present in discrete breathe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Localization in non-linear lattices of excitable elements is present in discrete breathers [1], and forms an interesting counterpart to localization of activity in neural systems [4]. We study the behavior of breatherlike excitations in a system of locally interacting integrate-and-fire neurons. Both numerical and analytical results justify the notion of a neural breather, which may form an element of working memory and attention.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a441a5eb3a2861003823e3667215bd76" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737092,"asset_id":2783313,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737092/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783313"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783313"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783313; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783313]").text(description); $(".js-view-count[data-work-id=2783313]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783313; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783313']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a441a5eb3a2861003823e3667215bd76" } } $('.js-work-strip[data-work-id=2783313]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783313,"title":"Discrete Breathers in Neural Networks","internal_url":"https://www.academia.edu/2783313/Discrete_Breathers_in_Neural_Networks","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737092,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"A0dpg05.pdf","download_url":"https://www.academia.edu/attachments/30737092/download_file","bulk_download_file_name":"Discrete_Breathers_in_Neural_Networks.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737092/A0dpg05-libre.pdf?1392083352=\u0026response-content-disposition=attachment%3B+filename%3DDiscrete_Breathers_in_Neural_Networks.pdf\u0026Expires=1740602063\u0026Signature=gSAukHqMBBmk-jsZZeLqb2vNBLgcdhrgV2eU18Eu2IDYMOWGaSvbZts92g0VQassUyH39LmItqNiyISsJaBseDOyRgSnj83HARqRdoyyQSgUR6hxhp3kXDXFqCxi3EWdO0HTP0OFRRvoTK9eyfMls2r0RpNpXaqjdxCusj0AxIkcnfWhdYKNmFVPJBNc8qYySBtgLJ6dJEpIxq6mGDhLC5njPAD-ANJH99aW4GWAzMUOsNGHiDJfjNOs1~V5OCxSDjREwpSATmaQvrpA7zj7D4j08OKGP1MWjbmggU~pkBTLAFnkcoYXQfmJMHdgXy~4zAcRjwQO3hYvEYsUBqRyrA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783312"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783312/Localized_Solutions_in_a_Simple_Neural_Field_Model"><img alt="Research paper thumbnail of Localized Solutions in a Simple Neural Field Model" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783312/Localized_Solutions_in_a_Simple_Neural_Field_Model">Localized Solutions in a Simple Neural Field Model</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract We investigate analytically properties like stability and existence of solutions of the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract We investigate analytically properties like stability and existence of solutions of the two dimensional neural field equation as proposed by Amari (1977) in [1] as a model of macroscopic activation dynamics in neural tissue. While the one dimensional case has been treated comprehensively, for the two dimensional case only the existence of circular solutions was shown, and stability was as well only considered for radially symmetric perturbations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="73c871400f9c88600b80d2b32bc9994e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737093,"asset_id":2783312,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737093/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783312"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783312"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783312; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783312]").text(description); $(".js-view-count[data-work-id=2783312]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783312; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783312']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "73c871400f9c88600b80d2b32bc9994e" } } $('.js-work-strip[data-work-id=2783312]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783312,"title":"Localized Solutions in a Simple Neural Field Model","internal_url":"https://www.academia.edu/2783312/Localized_Solutions_in_a_Simple_Neural_Field_Model","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737093,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"A0cns04.pdf","download_url":"https://www.academia.edu/attachments/30737093/download_file","bulk_download_file_name":"Localized_Solutions_in_a_Simple_Neural_F.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737093/A0cns04-libre.pdf?1392886301=\u0026response-content-disposition=attachment%3B+filename%3DLocalized_Solutions_in_a_Simple_Neural_F.pdf\u0026Expires=1740602063\u0026Signature=TKCWd8vqqQQp6eOG-5TKwt-w-f-tDlsczAeIB9oVAybpHa3LbPTupi0RJzLYu0Bwfmn0hoWfiAxGF79LKTlJaxGB7JKgn-PqvSHy6-QWy0sWC0wGDsuDQtYJX7fHf7pf2dkIsxPhkZT3pg7z1b3I-eSB2oVc86kgiB~NCwcz4AKLezNsz-aOJHDx4ePkLfQnkgmMMHvl7gXEuuYJLS4gAY~O640Qy32lHyEWgXPkkHCbbAakIkucZDJ-wn5m0YrIO6wYW9nyv3IPmofDuzYhkuxqaaXAeNFKn~r0mkR6xtbsnNX6LiR3QTR0OXIWk16NXJKgzgmuCmZTmqWcR6BGyg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783311"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/2783311/The_General_Model_for_Negative_Priming"><img alt="Research paper thumbnail of The General Model for Negative Priming" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/2783311/The_General_Model_for_Negative_Priming">The General Model for Negative Priming</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Negative priming is characterized by longer reaction times when responding to stimuli wh...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Negative priming is characterized by longer reaction times when responding to stimuli which have been actively ignored recently. A central problem of the interpretation of the NP effect is the lack of agreement about the underlying mechanisms. Over the past 20 years, various theoretical accounts have been developed to explain NP. However, empirical evidence does not clearly favour one theory over the others.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783311"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783311"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783311; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783311]").text(description); $(".js-view-count[data-work-id=2783311]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783311; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783311']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=2783311]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783311,"title":"The General Model for Negative Priming","internal_url":"https://www.academia.edu/2783311/The_General_Model_for_Negative_Priming","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783310"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783310/Switching_to_criticality_by_synchronized_input"><img alt="Research paper thumbnail of Switching to criticality by synchronized input" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783310/Switching_to_criticality_by_synchronized_input">Switching to criticality by synchronized input</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It was previously shown that an extended critical interval can be obtained in a neural network by...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It was previously shown that an extended critical interval can be obtained in a neural network by incorporation of depressive synapses [2]. In the present study we scrutinize a more realistic dynamics for the synaptic interactions that can be considered as the state-of-the-art in computational modeling of synaptic interaction (Figure 1)[2].</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e8be925edd427283ea54cd4f8db043e4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737094,"asset_id":2783310,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737094/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783310"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783310"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783310; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783310]").text(description); $(".js-view-count[data-work-id=2783310]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783310; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783310']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e8be925edd427283ea54cd4f8db043e4" } } $('.js-work-strip[data-work-id=2783310]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783310,"title":"Switching to criticality by synchronized input","internal_url":"https://www.academia.edu/2783310/Switching_to_criticality_by_synchronized_input","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737094,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"1471-2202-10-S1-P155.pdf","download_url":"https://www.academia.edu/attachments/30737094/download_file","bulk_download_file_name":"Switching_to_criticality_by_synchronized.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737094/1471-2202-10-S1-P155-libre.pdf?1391028630=\u0026response-content-disposition=attachment%3B+filename%3DSwitching_to_criticality_by_synchronized.pdf\u0026Expires=1740602063\u0026Signature=QYnru1MaDt~eiZR1dQwlT-LXwmF2hbDc9Lnpa0LvS1w8txWLhPjy2NbnvycOZu3BVFzM4~x9cQta3GQSMllBfYlGhsWeYmMlpbyv52su4BCMNJvumMQ3XyZ7Dmg8n9we1Asiny21jcB0ugBI0~3aImegvAfexJ2c6aUVdTx9q75PL1YFC25b8T8op7WDTTr-Zp1Oqrj6UVse5Z8kqPjH78kYGXZJN7gHEIxejkwLPQlGRw-KJCnX693~c90O6BG03UOf2y~ZO3eZ-wGsA7ZLIc7qTHvcO3iA6KJudhPuzWu6jgNpdmVyRtbOfENxydf2rfbrkQz74DhuKQYwSdrttg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783309"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783309/A_sensor_based_learning_algorithm_for_the_self_organization_of_robot_behavior"><img alt="Research paper thumbnail of A sensor-based learning algorithm for the self-organization of robot behavior" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783309/A_sensor_based_learning_algorithm_for_the_self_organization_of_robot_behavior">A sensor-based learning algorithm for the self-organization of robot behavior</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract: Ideally, sensory information forms the only source of information to a robot. We consid...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract: Ideally, sensory information forms the only source of information to a robot. We consider an algorithm for the self-organization of a controller. At short time scales the controller is merely reactive but the parameter dynamics and the acquisition of knowledge by an internal model lead to seemingly purposeful behavior on longer time scales. As a paradigmatic example, we study the simulation of an underactuated snake-like robot.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="24b3fd9f5b1fafe22621e41f840a57c0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737095,"asset_id":2783309,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737095/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783309"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783309"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783309; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783309]").text(description); $(".js-view-count[data-work-id=2783309]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783309; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783309']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "24b3fd9f5b1fafe22621e41f840a57c0" } } $('.js-work-strip[data-work-id=2783309]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783309,"title":"A sensor-based learning algorithm for the self-organization of robot behavior","internal_url":"https://www.academia.edu/2783309/A_sensor_based_learning_algorithm_for_the_self_organization_of_robot_behavior","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. Michael","middle_initials":null,"last_name":"Herrmann","page_name":"DrMichaelHerrmann","domain_name":"edinburgh","created_at":"2010-09-25T20:54:36.164-07:00","display_name":"J. Michael Herrmann","url":"https://edinburgh.academia.edu/DrMichaelHerrmann"},"attachments":[{"id":30737095,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"algorithms-02-00398.pdf","download_url":"https://www.academia.edu/attachments/30737095/download_file","bulk_download_file_name":"A_sensor_based_learning_algorithm_for_th.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30737095/algorithms-02-00398-libre.pdf?1392043260=\u0026response-content-disposition=attachment%3B+filename%3DA_sensor_based_learning_algorithm_for_th.pdf\u0026Expires=1740602063\u0026Signature=Gv3H4a00zOSQ9URYOQSb5~QoyidnOHW9Budc2Tyq8jJMhvOUk71lT7f4I64N5XQ1HdLzH1bwBwnIH-HQTLym5xOfq3LGHUHL0mwV~fxDCstNu79C-cq4TXMKPPtAE3qjIMFGtHqc2hL~0Hc1AxrY-ojEUsW0jGIdTaCRRFE~jHfUZ7Z2Yjmh3FPwHqVWWgJiflOCTXgWC6Cu2WxrGEmLP4OfZDcAxv~G4YxPXMzsF5bZQKYccRAnfI8Hg3Efx2lu6YZeqCHYvN9LNizv098LEEwAcT7sc7uXSe-9BOOrEjeO9tL6Iv9Vl9fbfRAdQdROhVdC8h9agHrT7ICCRTdu-A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="2783308"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/2783308/An_algorithm_for_generalized_principal_curves_with_adaptive_topology_in_complex_data_sets"><img alt="Research paper thumbnail of An algorithm for generalized principal curves with adaptive topology in complex data sets" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/2783308/An_algorithm_for_generalized_principal_curves_with_adaptive_topology_in_complex_data_sets">An algorithm for generalized principal curves with adaptive topology in complex data sets</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract. Generalized principal curves are capable of representing complex data structures as the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract. Generalized principal curves are capable of representing complex data structures as they may have branching points or may consist of disconnected parts. For their construction using an unsupervised learning algorithm the templates need to be structurally adaptive. The present algorithm meets this goal by a combination of a competitive Hebbian learning scheme and a self-organizing map algorithm.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9869e73eac9dd35e419319fb1b4aa305" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30737097,"asset_id":2783308,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30737097/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="2783308"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2783308"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2783308; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2783308]").text(description); $(".js-view-count[data-work-id=2783308]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2783308; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2783308']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9869e73eac9dd35e419319fb1b4aa305" } } $('.js-work-strip[data-work-id=2783308]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2783308,"title":"An algorithm for generalized principal curves with adaptive topology in complex data sets","internal_url":"https://www.academia.edu/2783308/An_algorithm_for_generalized_principal_curves_with_adaptive_topology_in_complex_data_sets","owner_id":253979,"coauthors_can_edit":true,"owner":{"id":253979,"first_name":"J. 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