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class="user-info-component-wrapper"><div class="user-summary-cta-container"><div class="user-summary-container"><div class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" alt="Walter Freeman" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/32972362/9802355/10922270/s200_walter.freeman.jpg_oh_1eec418a91386cd4862404f4aa0efcbb_oe_561dd41d___gda___1448916509_51a21827241acd978f11dc5a5a6d53dc" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Walter Freeman</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://berkeley.academia.edu/">University of California, Berkeley</a>, <a class="u-tcGrayDarker" href="https://berkeley.academia.edu/Departments/Molecular_and_Cell_Biology/Documents">Molecular and Cell Biology</a>, <span class="u-tcGrayDarker">Emeritus</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="Walter" data-follow-user-id="32972362" data-follow-user-source="profile_button" data-has-google="false"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">add</span>Follow</button><button class="ds2-5-button hidden profile-cta-button grow js-profile-unfollow-button" data-broccoli-component="user-info.unfollow-button" data-click-track="profile-user-info-unfollow-button" data-unfollow-user-id="32972362"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">done</span>Following</button></div></div><div class="user-stats-container"><a><div class="stat-container js-profile-followers"><p class="label">Followers</p><p class="data">63</p></div></a><a><div class="stat-container js-profile-followees" data-broccoli-component="user-info.followees-count" data-click-track="profile-expand-user-info-following"><p class="label">Following</p><p class="data">1</p></div></a><div class="js-mentions-count-container" style="display: none;"><a href="/WalterFreeman/mentions"><div class="stat-container"><p class="label">Mentions</p><p class="data"></p></div></a></div><span><div class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="user-bio-container"><div class="profile-bio fake-truncate js-profile-about" style="margin: 0px;"><b>Address: </b>United States<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="suggested-academics-container"><div class="suggested-academics--header"><p class="ds2-5-body-md-bold">Related Authors</p></div><ul class="suggested-user-card-list"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://ki.academia.edu/ErsenKavak"><img class="profile-avatar u-positionAbsolute" alt="Ersen Kavak" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/76000/84641/92533/s200_ersen.kavak.jpg" /></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://ki.academia.edu/ErsenKavak">Ersen Kavak</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Karolinska Institutet</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://berkeley.academia.edu/LucasHoran"><img class="profile-avatar u-positionAbsolute" alt="Lucas H Horan" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/6992397/15002239/15742046/s200_lucas.horan.jpg" /></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://berkeley.academia.edu/LucasHoran">Lucas H Horan</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of California, Berkeley</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://ut-ee.academia.edu/ToivoMaimets"><img class="profile-avatar u-positionAbsolute" alt="Toivo Maimets" border="0" onerror="if (this.src != 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Rybicki" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" src="https://gravatar.com/avatar/e9479dd16ee5dea177fc8870a55ca97b?s=200" /></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://uct.academia.edu/ERybicki">E. Rybicki</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of Cape Town</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://jamescook.academia.edu/AndreasLopata"><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://jamescook.academia.edu/AndreasLopata">Andreas Lopata</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">James Cook University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://wits.academia.edu/YasienSayed"><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://wits.academia.edu/YasienSayed">Yasien Sayed</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of the Witwatersrand</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://berkeley.academia.edu/CarlosBustamante"><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://berkeley.academia.edu/CarlosBustamante">Carlos Bustamante</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of California, Berkeley</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://berkeley.academia.edu/JeremyThorner"><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://berkeley.academia.edu/JeremyThorner">Jeremy Thorner</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of California, Berkeley</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://uct.academia.edu/ZendaWoodman"><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://uct.academia.edu/ZendaWoodman">Zenda Woodman</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of Cape Town</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://uel.academia.edu/JanaJavornik"><img class="profile-avatar u-positionAbsolute" alt="Jana Javornik" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/113072/30527/13170018/s200_jana.javornik.jpg" /></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://uel.academia.edu/JanaJavornik">Jana Javornik</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of East London</p></div></div></ul></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="32972362" href="https://www.academia.edu/Documents/in/Molecular_and_Cell_Biology"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://berkeley.academia.edu/WalterFreeman","location":"/WalterFreeman","scheme":"https","host":"berkeley.academia.edu","port":null,"pathname":"/WalterFreeman","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Molecular and Cell Biology"]}" data-trace="false" data-dom-id="Pill-react-component-059212e4-5b34-4d5f-8411-9e50bc86fbce"></div> <div id="Pill-react-component-059212e4-5b34-4d5f-8411-9e50bc86fbce"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" 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 Walter Freeman</h3></div><div class="js-work-strip profile--work_container" data-work-id="13901149"><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/13901149/Performance_of_Intelligent_Systems_Governed_by_Internally_Generated_Goals"><img alt="Research paper thumbnail of Performance of Intelligent Systems Governed by Internally Generated Goals" 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/13901149/Performance_of_Intelligent_Systems_Governed_by_Internally_Generated_Goals">Performance of Intelligent Systems Governed by Internally Generated Goals</a></div><div class="wp-workCard_item"><span>Computational Models for Neuroscience</span><span>, 2003</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="13901149"><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="13901149"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901149; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13901149]").text(description); $(".js-view-count[data-work-id=13901149]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13901149; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = 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href="https://www.academia.edu/13901148/Prolog"><img alt="Research paper thumbnail of Prolog" 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/13901148/Prolog">Prolog</a></div><div class="wp-workCard_item"><span>Perspectives in Neural Computing</span><span>, 2000</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="13901148"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa 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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" } } $('.js-work-strip[data-work-id=13901148]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13901148,"title":"Prolog","internal_url":"https://www.academia.edu/13901148/Prolog","owner_id":32972362,"coauthors_can_edit":true,"owner":{"id":32972362,"first_name":"Walter","middle_initials":null,"last_name":"Freeman","page_name":"WalterFreeman","domain_name":"berkeley","created_at":"2015-07-10T18:01:24.598-07:00","display_name":"Walter Freeman","url":"https://berkeley.academia.edu/WalterFreeman"},"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="13901147"><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/13901147/Making_Sense_of_Brain_Waves_The_Most_Baffling_Frontier_in_Neuroscience"><img alt="Research paper thumbnail of Making Sense of Brain Waves: The Most Baffling Frontier in Neuroscience" class="work-thumbnail" src="https://attachments.academia-assets.com/44821180/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/13901147/Making_Sense_of_Brain_Waves_The_Most_Baffling_Frontier_in_Neuroscience">Making Sense of Brain Waves: The Most Baffling Frontier in Neuroscience</a></div><div class="wp-workCard_item"><span>Biocomputing</span><span>, 2002</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Brains are characterized by every property that engineers and computer scientists detest and avoi...</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">Brains are characterized by every property that engineers and computer scientists detest and avoid. They are chaotic, unstable, nonlinear, nonstationary, non-Gaussian, asynchronous, noisy, and unpredictable in fine grain, yet undeniably they are among the most successful devices that a billion years of evolution has produced. No one can justifiably claim that he or she has modeled brains, but they are a flowing spring of new concepts, and they provide a gold standard of what we can aspire to accomplish in developing more intelligent machines. The most fertile source of ideas with which to challenge and break the restrictions that characterize modern engineering practice i s the electroencephalogram (EEG). It was the action potential of single neurons that provided the foundation of neurobiology for the 20th century, and in its time it supported the development of digital computers, neural networks, and computational neuroscience. Now in the 21st century, the EEG will lead us in a remarkably different direction of growth for the computing industry, which will be dominated by highly parallel, hierarchically organized, distributed analog machines. These devices now exist in prototype form. They feed on noise in support of chaotic attractor landscapes, which are shaped by reinforcement learning through self-governed experience, not training by 'teachers', and they may solve many of the problems of interfacing between finite state automata and the infinite complexity of the real world.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="58a75ed5306b6673a9074b2e48ffc552" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":44821180,"asset_id":13901147,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/44821180/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="13901147"><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="13901147"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901147; 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In the beginning is the chaos of neural activity, which is already present in the womb when ... The finger pulls a trigger and makes a person die; a flood erodes a bridge foundation and ...</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="13901146"><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="13901146"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901146; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13901146]").text(description); 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EEG WAVES reflect the mean excitation of pools of neurons. ... WHY EEG WAVES OSCILLATE Altern...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... EEG WAVES reflect the mean excitation of pools of neurons. ... WHY EEG WAVES OSCILLATE Alternating rises and falls in amplitude stem from negative-feedback circuits that are established by the interaction of pools of excitatory and inhibitory neurons. ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="13901140"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="13901140"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901140; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=13901134]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13901134,"title":"Three Types of State Transition Underlying Perception","internal_url":"https://www.academia.edu/13901134/Three_Types_of_State_Transition_Underlying_Perception","owner_id":32972362,"coauthors_can_edit":true,"owner":{"id":32972362,"first_name":"Walter","middle_initials":null,"last_name":"Freeman","page_name":"WalterFreeman","domain_name":"berkeley","created_at":"2015-07-10T18:01:24.598-07:00","display_name":"Walter Freeman","url":"https://berkeley.academia.edu/WalterFreeman"},"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="13901133"><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/13901133/Neurodynamics_An_Exploration_in_Mesoscopic_Brain_Dynamics"><img alt="Research paper thumbnail of Neurodynamics: An Exploration in Mesoscopic Brain Dynamics" 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/13901133/Neurodynamics_An_Exploration_in_Mesoscopic_Brain_Dynamics">Neurodynamics: An Exploration in Mesoscopic Brain Dynamics</a></div><div class="wp-workCard_item"><span>Perspectives in Neural Computing</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... Perspectives in Neural Computing Springer London Berlin Heidelberg New York Barcelona Hong Ko...</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">... Perspectives in Neural Computing Springer London Berlin Heidelberg New York Barcelona Hong Kong Milan Paris Singapore Tokyo One RKKF ... Studies of behavioral dynamics leading to the formation of multisensory percepts called Cestalts (Koffka 1935; Kohler 1940) gave ...</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="13901133"><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="13901133"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901133; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13901133]").text(description); $(".js-view-count[data-work-id=13901133]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13901133; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='13901133']"); 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" } } $('.js-work-strip[data-work-id=13901133]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13901133,"title":"Neurodynamics: An Exploration in Mesoscopic Brain Dynamics","internal_url":"https://www.academia.edu/13901133/Neurodynamics_An_Exploration_in_Mesoscopic_Brain_Dynamics","owner_id":32972362,"coauthors_can_edit":true,"owner":{"id":32972362,"first_name":"Walter","middle_initials":null,"last_name":"Freeman","page_name":"WalterFreeman","domain_name":"berkeley","created_at":"2015-07-10T18:01:24.598-07:00","display_name":"Walter Freeman","url":"https://berkeley.academia.edu/WalterFreeman"},"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="13901132"><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/13901132/Consciousness_Intentionality_and_Causality"><img alt="Research paper thumbnail of Consciousness, Intentionality, and Causality" class="work-thumbnail" src="https://attachments.academia-assets.com/44821183/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/13901132/Consciousness_Intentionality_and_Causality">Consciousness, Intentionality, and Causality</a></div><div class="wp-workCard_item"><span>Does Consciousness Cause Behavior?</span><span>, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To explain how stimuli cause consciousness, we have to explain causality. We can't trace linear c...</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">To explain how stimuli cause consciousness, we have to explain causality. We can't trace linear causal chains from receptors after the first cortical synapse, so we use circular causality to explain neural pattern formation by self-organizing dynamics. But an aspect of intentional action is causality, which we extrapolate to material objects in the world. Thus causality is a property of mind, not matter.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="14ad7513f00fa4211223eeddcfcdfc7e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":44821183,"asset_id":13901132,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/44821183/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="13901132"><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="13901132"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901132; 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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="13901131"><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/13901131/Guest_Editorial_Special_Issue_on_Temporal_Coding_for_Neural_Information_Processing"><img alt="Research paper thumbnail of Guest Editorial Special Issue on Temporal Coding for Neural Information Processing" class="work-thumbnail" src="https://attachments.academia-assets.com/39138168/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/13901131/Guest_Editorial_Special_Issue_on_Temporal_Coding_for_Neural_Information_Processing">Guest Editorial Special Issue on Temporal Coding for Neural Information Processing</a></div><div class="wp-workCard_item"><span>IEEE Transactions on Neural Networks</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We present a solution for the steady-state output rate of an ideal coincidence detector receiving...</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 present a solution for the steady-state output rate of an ideal coincidence detector receiving an arbitrary number of excitatory and inhibitory input spike trains. All excitatory spike trains have identical binomial count distributions (which includes ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="32a8a051428e8daf9213362216fe6171" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39138168,"asset_id":13901131,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39138168/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="13901131"><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="13901131"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901131; 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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="13901130"><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/13901130/Cortical_phase_transitions_non_equilibrium_thermodynamics_and_the_time_dependent_Ginzburg_Landau_equation"><img alt="Research paper thumbnail of Cortical phase transitions, non-equilibrium thermodynamics and the time-dependent Ginzburg-Landau equation" class="work-thumbnail" src="https://attachments.academia-assets.com/39138174/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/13901130/Cortical_phase_transitions_non_equilibrium_thermodynamics_and_the_time_dependent_Ginzburg_Landau_equation">Cortical phase transitions, non-equilibrium thermodynamics and the time-dependent Ginzburg-Landau equation</a></div><div class="wp-workCard_item"><span>International Journal of Modern Physics B</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The formation of amplitude modulated and phase modulated assemblies of neurons is observed in 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">The formation of amplitude modulated and phase modulated assemblies of neurons is observed in the brain functional activity. The study of the formation of such structures requires that the analysis has to be organized in hierarchical levels, microscopic, mesoscopic, macroscopic, each with its characteristic space-time scales and the various forms of energy, electric, chemical, thermal produced and used by the brain. In this paper, we discuss the microscopic dynamics underlying the mesoscopic and the macroscopic levels and focus our attention on the thermodynamics of the non-equilibrium phase transitions. We obtain the time-dependent Ginzburg-Landau equation for the non-stationary regime and consider the formation of topologically non-trivial structures such as the vortex solution. The power laws observed in functional activities of the brain is also discussed and related to coherent states characterizing the many-body dissipative model of brain.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7e12e20cbace1e3ec94c7552767f5e01" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39138174,"asset_id":13901130,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39138174/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="13901130"><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="13901130"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901130; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="3196464" id="papers"><div class="js-work-strip profile--work_container" data-work-id="13901149"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/13901149/Performance_of_Intelligent_Systems_Governed_by_Internally_Generated_Goals"><img alt="Research paper thumbnail of Performance of Intelligent Systems Governed by Internally Generated Goals" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/13901149/Performance_of_Intelligent_Systems_Governed_by_Internally_Generated_Goals">Performance of Intelligent Systems Governed by Internally Generated Goals</a></div><div class="wp-workCard_item"><span>Computational Models for Neuroscience</span><span>, 2003</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="13901149"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="13901149"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901149; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13901149]").text(description); $(".js-view-count[data-work-id=13901149]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13901149; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='13901149']"); 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); 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They are chaotic, unstable, nonlinear, nonstationary, non-Gaussian, asynchronous, noisy, and unpredictable in fine grain, yet undeniably they are among the most successful devices that a billion years of evolution has produced. No one can justifiably claim that he or she has modeled brains, but they are a flowing spring of new concepts, and they provide a gold standard of what we can aspire to accomplish in developing more intelligent machines. The most fertile source of ideas with which to challenge and break the restrictions that characterize modern engineering practice i s the electroencephalogram (EEG). It was the action potential of single neurons that provided the foundation of neurobiology for the 20th century, and in its time it supported the development of digital computers, neural networks, and computational neuroscience. Now in the 21st century, the EEG will lead us in a remarkably different direction of growth for the computing industry, which will be dominated by highly parallel, hierarchically organized, distributed analog machines. These devices now exist in prototype form. They feed on noise in support of chaotic attractor landscapes, which are shaped by reinforcement learning through self-governed experience, not training by 'teachers', and they may solve many of the problems of interfacing between finite state automata and the infinite complexity of the real world.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="58a75ed5306b6673a9074b2e48ffc552" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":44821180,"asset_id":13901147,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/44821180/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="13901147"><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="13901147"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901147; 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In the beginning is the chaos of neural activity, which is already present in the womb when ... The finger pulls a trigger and makes a person die; a flood erodes a bridge foundation and ...</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="13901146"><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="13901146"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901146; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13901146]").text(description); 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EEG WAVES reflect the mean excitation of pools of neurons. ... WHY EEG WAVES OSCILLATE Altern...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... EEG WAVES reflect the mean excitation of pools of neurons. ... WHY EEG WAVES OSCILLATE Alternating rises and falls in amplitude stem from negative-feedback circuits that are established by the interaction of pools of excitatory and inhibitory neurons. ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="13901140"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="13901140"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901140; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=13901134]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13901134,"title":"Three Types of State Transition Underlying Perception","internal_url":"https://www.academia.edu/13901134/Three_Types_of_State_Transition_Underlying_Perception","owner_id":32972362,"coauthors_can_edit":true,"owner":{"id":32972362,"first_name":"Walter","middle_initials":null,"last_name":"Freeman","page_name":"WalterFreeman","domain_name":"berkeley","created_at":"2015-07-10T18:01:24.598-07:00","display_name":"Walter Freeman","url":"https://berkeley.academia.edu/WalterFreeman"},"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="13901133"><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/13901133/Neurodynamics_An_Exploration_in_Mesoscopic_Brain_Dynamics"><img alt="Research paper thumbnail of Neurodynamics: An Exploration in Mesoscopic Brain Dynamics" 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/13901133/Neurodynamics_An_Exploration_in_Mesoscopic_Brain_Dynamics">Neurodynamics: An Exploration in Mesoscopic Brain Dynamics</a></div><div class="wp-workCard_item"><span>Perspectives in Neural Computing</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... Perspectives in Neural Computing Springer London Berlin Heidelberg New York Barcelona Hong Ko...</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">... Perspectives in Neural Computing Springer London Berlin Heidelberg New York Barcelona Hong Kong Milan Paris Singapore Tokyo One RKKF ... Studies of behavioral dynamics leading to the formation of multisensory percepts called Cestalts (Koffka 1935; Kohler 1940) gave ...</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="13901133"><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="13901133"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901133; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13901133]").text(description); $(".js-view-count[data-work-id=13901133]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13901133; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='13901133']"); 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" } } $('.js-work-strip[data-work-id=13901133]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13901133,"title":"Neurodynamics: An Exploration in Mesoscopic Brain Dynamics","internal_url":"https://www.academia.edu/13901133/Neurodynamics_An_Exploration_in_Mesoscopic_Brain_Dynamics","owner_id":32972362,"coauthors_can_edit":true,"owner":{"id":32972362,"first_name":"Walter","middle_initials":null,"last_name":"Freeman","page_name":"WalterFreeman","domain_name":"berkeley","created_at":"2015-07-10T18:01:24.598-07:00","display_name":"Walter Freeman","url":"https://berkeley.academia.edu/WalterFreeman"},"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="13901132"><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/13901132/Consciousness_Intentionality_and_Causality"><img alt="Research paper thumbnail of Consciousness, Intentionality, and Causality" class="work-thumbnail" src="https://attachments.academia-assets.com/44821183/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/13901132/Consciousness_Intentionality_and_Causality">Consciousness, Intentionality, and Causality</a></div><div class="wp-workCard_item"><span>Does Consciousness Cause Behavior?</span><span>, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To explain how stimuli cause consciousness, we have to explain causality. We can't trace linear c...</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">To explain how stimuli cause consciousness, we have to explain causality. We can't trace linear causal chains from receptors after the first cortical synapse, so we use circular causality to explain neural pattern formation by self-organizing dynamics. But an aspect of intentional action is causality, which we extrapolate to material objects in the world. Thus causality is a property of mind, not matter.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="14ad7513f00fa4211223eeddcfcdfc7e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":44821183,"asset_id":13901132,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/44821183/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="13901132"><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="13901132"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901132; 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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="13901131"><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/13901131/Guest_Editorial_Special_Issue_on_Temporal_Coding_for_Neural_Information_Processing"><img alt="Research paper thumbnail of Guest Editorial Special Issue on Temporal Coding for Neural Information Processing" class="work-thumbnail" src="https://attachments.academia-assets.com/39138168/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/13901131/Guest_Editorial_Special_Issue_on_Temporal_Coding_for_Neural_Information_Processing">Guest Editorial Special Issue on Temporal Coding for Neural Information Processing</a></div><div class="wp-workCard_item"><span>IEEE Transactions on Neural Networks</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We present a solution for the steady-state output rate of an ideal coincidence detector receiving...</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 present a solution for the steady-state output rate of an ideal coincidence detector receiving an arbitrary number of excitatory and inhibitory input spike trains. All excitatory spike trains have identical binomial count distributions (which includes ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="32a8a051428e8daf9213362216fe6171" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39138168,"asset_id":13901131,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39138168/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="13901131"><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="13901131"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901131; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13901131]").text(description); $(".js-view-count[data-work-id=13901131]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 13901131; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='13901131']"); 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: "32a8a051428e8daf9213362216fe6171" } } $('.js-work-strip[data-work-id=13901131]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":13901131,"title":"Guest Editorial Special Issue on Temporal Coding for Neural Information Processing","internal_url":"https://www.academia.edu/13901131/Guest_Editorial_Special_Issue_on_Temporal_Coding_for_Neural_Information_Processing","owner_id":32972362,"coauthors_can_edit":true,"owner":{"id":32972362,"first_name":"Walter","middle_initials":null,"last_name":"Freeman","page_name":"WalterFreeman","domain_name":"berkeley","created_at":"2015-07-10T18:01:24.598-07:00","display_name":"Walter Freeman","url":"https://berkeley.academia.edu/WalterFreeman"},"attachments":[{"id":39138168,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/39138168/thumbnails/1.jpg","file_name":"02bfe50cb88a2d7afd000000.pdf20151012-16080-yal80b.pdf","download_url":"https://www.academia.edu/attachments/39138168/download_file","bulk_download_file_name":"Guest_Editorial_Special_Issue_on_Tempora.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/39138168/02bfe50cb88a2d7afd000000.pdf20151012-16080-yal80b-libre.pdf?1444689042=\u0026response-content-disposition=attachment%3B+filename%3DGuest_Editorial_Special_Issue_on_Tempora.pdf\u0026Expires=1739840081\u0026Signature=f8RxtaR5DzCHXhYfyTOD5Q~vbY5CPf9l3A2uwstzcaSkHjoFbIx33H7rBmGRxmWCcTLStCw2S21wc8Y6rTmiFDr0wInnFWuMGvh54cIyoTLEFloFi7X8i~GyEmgLefSUntZzcl4HLuaF23WHfweqIqcHnMBtqQ6tzzMilmhyJUEzEddHaYxIE-J9OwxKw1DwIXR2imbLfnqWzlCrAayiATOTwa3CjdT5VeAEA4vXItWeURev3waANbQdZ-lfI94YvI-vAKDfV40aXe5N9QuTx0BbV7823EmrvIEBoum6uZBjVrex14cs8UEIR3p4mGExUakGoE08ClexTKCkzscQ0Q__\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="13901130"><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/13901130/Cortical_phase_transitions_non_equilibrium_thermodynamics_and_the_time_dependent_Ginzburg_Landau_equation"><img alt="Research paper thumbnail of Cortical phase transitions, non-equilibrium thermodynamics and the time-dependent Ginzburg-Landau equation" class="work-thumbnail" src="https://attachments.academia-assets.com/39138174/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/13901130/Cortical_phase_transitions_non_equilibrium_thermodynamics_and_the_time_dependent_Ginzburg_Landau_equation">Cortical phase transitions, non-equilibrium thermodynamics and the time-dependent Ginzburg-Landau equation</a></div><div class="wp-workCard_item"><span>International Journal of Modern Physics B</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The formation of amplitude modulated and phase modulated assemblies of neurons is observed in 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">The formation of amplitude modulated and phase modulated assemblies of neurons is observed in the brain functional activity. The study of the formation of such structures requires that the analysis has to be organized in hierarchical levels, microscopic, mesoscopic, macroscopic, each with its characteristic space-time scales and the various forms of energy, electric, chemical, thermal produced and used by the brain. In this paper, we discuss the microscopic dynamics underlying the mesoscopic and the macroscopic levels and focus our attention on the thermodynamics of the non-equilibrium phase transitions. We obtain the time-dependent Ginzburg-Landau equation for the non-stationary regime and consider the formation of topologically non-trivial structures such as the vortex solution. The power laws observed in functional activities of the brain is also discussed and related to coherent states characterizing the many-body dissipative model of brain.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7e12e20cbace1e3ec94c7552767f5e01" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":39138174,"asset_id":13901130,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/39138174/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="13901130"><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="13901130"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13901130; 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