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type="text/json">{"id":10904,"name":"Electroencephalography","url":"https://www.academia.edu/Documents/in/Electroencephalography?f_ri=995270","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="13493" href="https://www.academia.edu/Documents/in/Motion_perception">Motion perception</a><script data-card-contents-for-ri="13493" type="text/json">{"id":13493,"name":"Motion perception","url":"https://www.academia.edu/Documents/in/Motion_perception?f_ri=995270","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=56817549]'), work: {"id":56817549,"title":"The parallel visual motion inputs into areas V1 and V5 of human cerebral 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href="https://www.academia.edu/69965718/Initial_ocular_following_in_humans_a_response_to_first_order_motion_energy">Initial ocular following in humans: a response to first-order motion energy</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Visual motion is sensed by low-level (energy-based) and high-level (feature-based) mechanisms. Ocular following responses (OFR) were elicited in humans by applying horizontal motion to vertical square-wave gratings lacking the fundamental... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_69965718" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Visual motion is sensed by low-level (energy-based) and high-level (feature-based) mechanisms. Ocular following responses (OFR) were elicited in humans by applying horizontal motion to vertical square-wave gratings lacking the fundamental (&quot;missing fundamental stimulus&quot;). Motion consisted of successive 1/4-wavelength steps, so the features and 4n+1 harmonics (where n=integer) shifted forwards, whereas the 4n-1 harmonics--including the strongest Fourier component (the 3rd harmonic)--shifted backwards (spatial aliasing). Initial OFR, recorded with the electromagnetic search coil technique, were always in the direction of the 3rd harmonic, e.g., leftward steps resulted in rightward OFR. Thus, the earliest OFR were strongly dependent on the motion of the major Fourier component, consistent with early spatio-temporal filtering prior to motion detection, as in the well-known energy model of motion analysis.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/69965718" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f5c69ecff947f708d931feb777b014ab" rel="nofollow" data-download="{"attachment_id":79864509,"asset_id":69965718,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/79864509/download_file?st=MTczMjQxMzAzNiw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="158369694" href="https://independent.academia.edu/kevinChen354">kevin Chen</a><script data-card-contents-for-user="158369694" type="text/json">{"id":158369694,"first_name":"kevin","last_name":"Chen","domain_name":"independent","page_name":"kevinChen354","display_name":"kevin Chen","profile_url":"https://independent.academia.edu/kevinChen354?f_ri=995270","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_69965718 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="69965718"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 69965718, container: ".js-paper-rank-work_69965718", }); 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Ocular following responses (OFR) were elicited in humans by applying horizontal motion to vertical square-wave gratings lacking the fundamental (\u0026quot;missing fundamental stimulus\u0026quot;). Motion consisted of successive 1/4-wavelength steps, so the features and 4n+1 harmonics (where n=integer) shifted forwards, whereas the 4n-1 harmonics--including the strongest Fourier component (the 3rd harmonic)--shifted backwards (spatial aliasing). Initial OFR, recorded with the electromagnetic search coil technique, were always in the direction of the 3rd harmonic, e.g., leftward steps resulted in rightward OFR. Thus, the earliest OFR were strongly dependent on the motion of the major Fourier component, consistent with early spatio-temporal filtering prior to motion detection, as in the well-known energy model of motion analysis.","downloadable_attachments":[{"id":79864509,"asset_id":69965718,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":158369694,"first_name":"kevin","last_name":"Chen","domain_name":"independent","page_name":"kevinChen354","display_name":"kevin Chen","profile_url":"https://independent.academia.edu/kevinChen354?f_ri=995270","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":246,"name":"Psychophysics","url":"https://www.academia.edu/Documents/in/Psychophysics?f_ri=995270","nofollow":false},{"id":13493,"name":"Motion perception","url":"https://www.academia.edu/Documents/in/Motion_perception?f_ri=995270","nofollow":false},{"id":13766,"name":"Motion 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Motion","url":"https://www.academia.edu/Documents/in/Visual_Motion?f_ri=995270"},{"id":2444775,"name":"Psychomotor Performance","url":"https://www.academia.edu/Documents/in/Psychomotor_Performance?f_ri=995270"},{"id":2849038,"name":"photic stimulation","url":"https://www.academia.edu/Documents/in/photic_stimulation?f_ri=995270"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences?f_ri=995270"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences?f_ri=995270"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_13092570 coauthored" data-work_id="13092570" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest 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class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="246" href="https://www.academia.edu/Documents/in/Psychophysics">Psychophysics</a>, <script data-card-contents-for-ri="246" type="text/json">{"id":246,"name":"Psychophysics","url":"https://www.academia.edu/Documents/in/Psychophysics?f_ri=995270","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="6200" href="https://www.academia.edu/Documents/in/Magnetic_Resonance_Imaging">Magnetic Resonance Imaging</a>, <script data-card-contents-for-ri="6200" type="text/json">{"id":6200,"name":"Magnetic Resonance Imaging","url":"https://www.academia.edu/Documents/in/Magnetic_Resonance_Imaging?f_ri=995270","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="13493" href="https://www.academia.edu/Documents/in/Motion_perception">Motion perception</a>, <script data-card-contents-for-ri="13493" type="text/json">{"id":13493,"name":"Motion perception","url":"https://www.academia.edu/Documents/in/Motion_perception?f_ri=995270","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="28235" href="https://www.academia.edu/Documents/in/Multidisciplinary">Multidisciplinary</a><script data-card-contents-for-ri="28235" type="text/json">{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=995270","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=13092570]'), work: {"id":13092570,"title":"Visual motion aftereffect in human cortical area MT revealed by functional magnetic resonance 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itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32201043" href="https://unitn.academia.edu/MassimoVescovi">Massimo Vescovi</a><script data-card-contents-for-user="32201043" type="text/json">{"id":32201043,"first_name":"Massimo","last_name":"Vescovi","domain_name":"unitn","page_name":"MassimoVescovi","display_name":"Massimo Vescovi","profile_url":"https://unitn.academia.edu/MassimoVescovi?f_ri=995270","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_12987590 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="12987590"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 12987590, container: ".js-paper-rank-work_12987590", }); });</script></li><li class="js-percentile-work_12987590 InlineList-item InlineList-item--bordered hidden 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href="https://www.academia.edu/24180223/Visual_motion_and_the_human_brain_what_has_neuroimaging_told_us">Visual motion and the human brain: what has neuroimaging told us?</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/24180223" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="bd6aab3f77bd675ddc4126452a446afe" rel="nofollow" data-download="{"attachment_id":44518742,"asset_id":24180223,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen 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Dukelow","profile_url":"https://independent.academia.edu/SDukelow?f_ri=995270","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=995270","nofollow":false},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=995270","nofollow":false},{"id":5359,"name":"Visual perception","url":"https://www.academia.edu/Documents/in/Visual_perception?f_ri=995270","nofollow":false},{"id":6200,"name":"Magnetic Resonance Imaging","url":"https://www.academia.edu/Documents/in/Magnetic_Resonance_Imaging?f_ri=995270","nofollow":false},{"id":7736,"name":"Attention","url":"https://www.academia.edu/Documents/in/Attention?f_ri=995270"},{"id":13493,"name":"Motion perception","url":"https://www.academia.edu/Documents/in/Motion_perception?f_ri=995270"},{"id":21548,"name":"Cognitive Neuroscience","url":"https://www.academia.edu/Documents/in/Cognitive_Neuroscience?f_ri=995270"},{"id":61474,"name":"Brain","url":"https://www.academia.edu/Documents/in/Brain?f_ri=995270"},{"id":64568,"name":"Humans","url":"https://www.academia.edu/Documents/in/Humans?f_ri=995270"},{"id":89158,"name":"Human Motion","url":"https://www.academia.edu/Documents/in/Human_Motion?f_ri=995270"},{"id":96047,"name":"Case Study","url":"https://www.academia.edu/Documents/in/Case_Study?f_ri=995270"},{"id":203010,"name":"Human Brain","url":"https://www.academia.edu/Documents/in/Human_Brain?f_ri=995270"},{"id":203765,"name":"Diagnostic Imaging","url":"https://www.academia.edu/Documents/in/Diagnostic_Imaging?f_ri=995270"},{"id":904693,"name":"Top Down","url":"https://www.academia.edu/Documents/in/Top_Down?f_ri=995270"},{"id":995270,"name":"Visual Motion","url":"https://www.academia.edu/Documents/in/Visual_Motion?f_ri=995270"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_14814488 coauthored" data-work_id="14814488" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/14814488/Repetitive_optokinetic_stimulation_induces_lasting_recovery_from_visual_neglect">Repetitive optokinetic stimulation induces lasting recovery from visual neglect</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Purpose: To evaluate whether repetitive optokinetic stimulation with active pursuit eye movements leads to substantial and greater recovery from visual neglect as compared to conventional visual scanning training. Methods: Two groups of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_14814488" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Purpose: To evaluate whether repetitive optokinetic stimulation with active pursuit eye movements leads to substantial and greater recovery from visual neglect as compared to conventional visual scanning training. Methods: Two groups of five patients with leftsided hemineglect were consecutively collected and matched for clinical and demographic variables as well as neglect severity. One group received five treatment sessions of repetitive optokinetic stimulation (R-OKS) within one week, while the other group received the same amount of conventional visual scanning training (VST) using identical visual stimuli and setup. All patients were treated in a single-subject baseline design with treatment-free intervals before (14 days) and after specific neglect therapy (14 days). Dependent variables were the improvements in digit cancellation, visuoperceptual and visuomotor line bisection and visual size distortion during treatment. The transfer of treatment effects was assessed by a parag...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/14814488" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="152e701b3759768b44d1295213209590" rel="nofollow" data-download="{"attachment_id":38434198,"asset_id":14814488,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38434198/download_file?st=MTczMjQxMzAzNiw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33394472" href="https://uni-regensburg.academia.edu/IKeller">I. 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u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Training on a visual task leads to increased perceptual and neural responses to visual features that were attended during training as well as decreased responses to neglected distractor features. However, the time course of these... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_64761348" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Training on a visual task leads to increased perceptual and neural responses to visual features that were attended during training as well as decreased responses to neglected distractor features. However, the time course of these attention-based modulations of neural sensitivity for visual features has not been investigated before. Here we measured event related potentials (ERP) in response to motion stimuli with different coherence levels before and after training on a speed discrimination task requiring object-based attentional selection of one of the two competing motion stimuli. We found that two peaks on the ERP waveform were modulated by the strength of the coherent motion signal; the response amplitude associated with motion directions that were neglected during training was smaller than the response amplitude associated with motion directions that were attended during training. The first peak of motion coherence-dependent modulation of the ERP responses was at 300 ms after s...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/64761348" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="41d85d1575699693c33e6173fa1d02e2" rel="nofollow" data-download="{"attachment_id":76646418,"asset_id":64761348,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/76646418/download_file?st=MTczMjQxMzAzNiw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34261551" href="https://smc.academia.edu/MMurphy">Miguel Murphy</a><script data-card-contents-for-user="34261551" type="text/json">{"id":34261551,"first_name":"Miguel","last_name":"Murphy","domain_name":"smc","page_name":"MMurphy","display_name":"Miguel Murphy","profile_url":"https://smc.academia.edu/MMurphy?f_ri=995270","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_64761348 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="64761348"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 64761348, container: ".js-paper-rank-work_64761348", }); 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The first peak of motion coherence-dependent modulation of the ERP responses was at 300 ms after s...","downloadable_attachments":[{"id":76646418,"asset_id":64761348,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34261551,"first_name":"Miguel","last_name":"Murphy","domain_name":"smc","page_name":"MMurphy","display_name":"Miguel Murphy","profile_url":"https://smc.academia.edu/MMurphy?f_ri=995270","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":44431,"name":"Perceptual Learning","url":"https://www.academia.edu/Documents/in/Perceptual_Learning?f_ri=995270","nofollow":false},{"id":123274,"name":"Parietal Cortex","url":"https://www.academia.edu/Documents/in/Parietal_Cortex?f_ri=995270","nofollow":false},{"id":995270,"name":"Visual Motion","url":"https://www.academia.edu/Documents/in/Visual_Motion?f_ri=995270","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div 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})();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_30182869 coauthored" data-work_id="30182869" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/30182869/Efficient_Spiking_Neural_Network_Model_of_Pattern_Motion_Selectivity_in_Visual_Cortex">Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_30182869" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation out-performs a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40×40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/30182869" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0148b090875dc4791433c048b2120566" rel="nofollow" data-download="{"attachment_id":50641757,"asset_id":30182869,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50641757/download_file?st=MTczMjQxMzAzNiw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="57466114" href="https://washington.academia.edu/MichaelBeyeler">Michael Beyeler</a><script data-card-contents-for-user="57466114" type="text/json">{"id":57466114,"first_name":"Michael","last_name":"Beyeler","domain_name":"washington","page_name":"MichaelBeyeler","display_name":"Michael Beyeler","profile_url":"https://washington.academia.edu/MichaelBeyeler?f_ri=995270","photo":"https://0.academia-photos.com/57466114/15077680/15795261/s65_michael.beyeler.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-30182869">+1</span><div class="hidden js-additional-users-30182869"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/MicahRichert">Micah Richert</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-30182869'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-30182869').html(); 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A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation out-performs a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40×40 input video in real-time using a single off-the-shelf GPU. 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itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/44278201/Architecture_un_Visual_Motion_Gaming_and_Animation_Hub">Architecture un Visual Motion : Gaming and Animation Hub</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In the past few days, a great revolution is seen in the field of animation and gaming that deals with visual motion, which is helping in the development of society as a whole, and progress is seen on a larger scale, affecting a lot of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_44278201" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In the past few days, a great revolution is seen in the field of animation and gaming that deals with visual motion, which is helping in the development of society as a whole, and progress is seen on a larger scale, affecting a lot of people. A tremendous advance of technology has helped both of these fields to develop significantly. Based on similar terms is the idea of pop culture that is experiencing a boost in India and is being appreciated as well. International Companies like Google, Facebook, Pixar, Disney, etc. are developing their ideas towards this culture. Companies like Ubisoft, Sony have expanded internationally on a larger scale as compared to India. But according to the present-day condition of India, we don’t have bigger platforms for the upcoming creative brains, which can help ideas to flourish and are somewhere taking a back seat.  The idea is to propose a platform for gaming and animation, to overcome this challenge, which will be the first of its kind in India where people will be trained and will gain exposure by collaborating with bigger projects to enhance the development of the field.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/44278201" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f86431ac877cdb3ee588e4fdcedbfafc" rel="nofollow" data-download="{"attachment_id":64656776,"asset_id":44278201,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/64656776/download_file?st=MTczMjQxMzAzNyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="67389138" href="https://independent.academia.edu/AnshulChole">Anshul Chole</a><script data-card-contents-for-user="67389138" type="text/json">{"id":67389138,"first_name":"Anshul","last_name":"Chole","domain_name":"independent","page_name":"AnshulChole","display_name":"Anshul Chole","profile_url":"https://independent.academia.edu/AnshulChole?f_ri=995270","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-44278201">+1</span><div class="hidden js-additional-users-44278201"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://mumbaiunivercity.academia.edu/npanium">Nishant Pandav</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-44278201'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-44278201').html(); 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The input is a colour image and the output is the3D pose of the arm in the current image. To handle the obviousambiguities involved in 3D estimation based on 2Ddata a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_66581753" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper is concerned with visual motion capture of a humanarm. The input is a colour image and the output is the3D pose of the arm in the current image. To handle the obviousambiguities involved in 3D estimation based on 2Ddata a model-based approach is adapted where all configurationsof the arm are matched with the image data. 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The input is a colour image and the output is the3D pose of the arm in the current image. To handle the obviousambiguities involved in 3D estimation based on 2Ddata a model-based approach is adapted where all configurationsof the arm are matched with the image data. Theseconfigurations are efficiently","downloadable_attachments":[{"id":77716686,"asset_id":66581753,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":42914508,"first_name":"Erik","last_name":"Granum","domain_name":"aalborg","page_name":"ErikGranum","display_name":"Erik Granum","profile_url":"https://aalborg.academia.edu/ErikGranum?f_ri=995270","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=995270","nofollow":false},{"id":230436,"name":"Pose Estimation","url":"https://www.academia.edu/Documents/in/Pose_Estimation?f_ri=995270","nofollow":false},{"id":813651,"name":"Model Based Approach","url":"https://www.academia.edu/Documents/in/Model_Based_Approach?f_ri=995270","nofollow":false},{"id":995270,"name":"Visual Motion","url":"https://www.academia.edu/Documents/in/Visual_Motion?f_ri=995270","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_30182624" data-work_id="30182624" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/30182624/A_GPU_accelerated_cortical_neural_network_model_for_visually_guided_robot_navigation">A GPU-accelerated cortical neural network model for visually guided robot navigation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Humans and other terrestrial animals use vision to traverse novel cluttered environments with apparent ease. On one hand, although much is known about the behavioral dynamics of steering in humans, it remains unclear how relevant... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_30182624" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Humans and other terrestrial animals use vision to traverse novel cluttered environments with apparent ease. On one hand, although much is known about the behavioral dynamics of steering in humans, it remains unclear how relevant perceptual variables might be represented in the brain. On the other hand, although a wealth of data exists about the neural circuitry that is concerned with the perception of self-motion variables such as the current direction of travel, little research has been devoted to investigating how this neural circuitry may relate to active steering control. Here we present a cortical neural network model for visually guided navigation that has been embodied on a physical robot exploring a real-world environment. The model includes a rate based motion energy model for area V1, and a spiking neural network model for cortical area MT. The model generates a cortical representation of optic flow, determines the position of objects based on motion discontinuities, and combines these signals with the representation of a goal location to produce motor commands that successfully steer the robot around obstacles toward the goal. The model produces robot trajectories that closely match human behavioral data. This study demonstrates how neural signals in a model of cortical area MT might provide sufficient motion information to steer a physical robot on human-like paths around obstacles in a real-world environment, and exemplifies the importance of embodiment, as behavior is deeply coupled not only with the underlying model of brain function, but also with the anatomical constraints of the physical body it controls.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/30182624" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="25e0d27065fb8417ed2c7dbda66fe9dd" rel="nofollow" data-download="{"attachment_id":50641560,"asset_id":30182624,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50641560/download_file?st=MTczMjQxMzAzNyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="57466114" href="https://washington.academia.edu/MichaelBeyeler">Michael Beyeler</a><script data-card-contents-for-user="57466114" type="text/json">{"id":57466114,"first_name":"Michael","last_name":"Beyeler","domain_name":"washington","page_name":"MichaelBeyeler","display_name":"Michael Beyeler","profile_url":"https://washington.academia.edu/MichaelBeyeler?f_ri=995270","photo":"https://0.academia-photos.com/57466114/15077680/15795261/s65_michael.beyeler.jpg"}</script></span></span></li><li class="js-paper-rank-work_30182624 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="30182624"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 30182624, container: ".js-paper-rank-work_30182624", }); 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$(".js-view-count[data-work-id=30182624]").text(description); $(".js-view-count-work_30182624").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_30182624").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="30182624"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">13</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="77" href="https://www.academia.edu/Documents/in/Robotics">Robotics</a>, <script data-card-contents-for-ri="77" type="text/json">{"id":77,"name":"Robotics","url":"https://www.academia.edu/Documents/in/Robotics?f_ri=995270","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2950" href="https://www.academia.edu/Documents/in/Computational_Modeling">Computational Modeling</a>, <script data-card-contents-for-ri="2950" type="text/json">{"id":2950,"name":"Computational Modeling","url":"https://www.academia.edu/Documents/in/Computational_Modeling?f_ri=995270","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4955" href="https://www.academia.edu/Documents/in/Computational_Modelling">Computational Modelling</a>, <script data-card-contents-for-ri="4955" type="text/json">{"id":4955,"name":"Computational Modelling","url":"https://www.academia.edu/Documents/in/Computational_Modelling?f_ri=995270","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="11598" href="https://www.academia.edu/Documents/in/Neural_Networks">Neural Networks</a><script data-card-contents-for-ri="11598" type="text/json">{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=995270","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=30182624]'), work: {"id":30182624,"title":"A GPU-accelerated cortical neural network model for visually guided robot navigation","created_at":"2016-11-30T11:42:59.012-08:00","url":"https://www.academia.edu/30182624/A_GPU_accelerated_cortical_neural_network_model_for_visually_guided_robot_navigation?f_ri=995270","dom_id":"work_30182624","summary":"Humans and other terrestrial animals use vision to traverse novel cluttered environments with apparent ease. On one hand, although much is known about the behavioral dynamics of steering in humans, it remains unclear how relevant perceptual variables might be represented in the brain. On the other hand, although a wealth of data exists about the neural circuitry that is concerned with the perception of self-motion variables such as the current direction of travel, little research has been devoted to investigating how this neural circuitry may relate to active steering control. Here we present a cortical neural network model for visually guided navigation that has been embodied on a physical robot exploring a real-world environment. The model includes a rate based motion energy model for area V1, and a spiking neural network model for cortical area MT. The model generates a cortical representation of optic flow, determines the position of objects based on motion discontinuities, and combines these signals with the representation of a goal location to produce motor commands that successfully steer the robot around obstacles toward the goal. The model produces robot trajectories that closely match human behavioral data. This study demonstrates how neural signals in a model of cortical area MT might provide sufficient motion information to steer a physical robot on human-like paths around obstacles in a real-world environment, and exemplifies the importance of embodiment, as behavior is deeply coupled not only with the underlying model of brain function, but also with the anatomical constraints of the physical body it controls.","downloadable_attachments":[{"id":50641560,"asset_id":30182624,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":57466114,"first_name":"Michael","last_name":"Beyeler","domain_name":"washington","page_name":"MichaelBeyeler","display_name":"Michael Beyeler","profile_url":"https://washington.academia.edu/MichaelBeyeler?f_ri=995270","photo":"https://0.academia-photos.com/57466114/15077680/15795261/s65_michael.beyeler.jpg"}],"research_interests":[{"id":77,"name":"Robotics","url":"https://www.academia.edu/Documents/in/Robotics?f_ri=995270","nofollow":false},{"id":2950,"name":"Computational Modeling","url":"https://www.academia.edu/Documents/in/Computational_Modeling?f_ri=995270","nofollow":false},{"id":4955,"name":"Computational Modelling","url":"https://www.academia.edu/Documents/in/Computational_Modelling?f_ri=995270","nofollow":false},{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=995270","nofollow":false},{"id":11988,"name":"Robotics Navigation","url":"https://www.academia.edu/Documents/in/Robotics_Navigation?f_ri=995270"},{"id":12225,"name":"GPU Computing","url":"https://www.academia.edu/Documents/in/GPU_Computing?f_ri=995270"},{"id":21081,"name":"GPGPU (General Purpose GPU) Programming","url":"https://www.academia.edu/Documents/in/GPGPU_General_Purpose_GPU_Programming?f_ri=995270"},{"id":45148,"name":"Neurorobotics","url":"https://www.academia.edu/Documents/in/Neurorobotics?f_ri=995270"},{"id":49962,"name":"Visual Cortex","url":"https://www.academia.edu/Documents/in/Visual_Cortex?f_ri=995270"},{"id":61411,"name":"Neuromorphic Computing","url":"https://www.academia.edu/Documents/in/Neuromorphic_Computing?f_ri=995270"},{"id":317562,"name":"Spiking Neural Networks","url":"https://www.academia.edu/Documents/in/Spiking_Neural_Networks?f_ri=995270"},{"id":995270,"name":"Visual Motion","url":"https://www.academia.edu/Documents/in/Visual_Motion?f_ri=995270"},{"id":1165829,"name":"Reactive Robot Navigation","url":"https://www.academia.edu/Documents/in/Reactive_Robot_Navigation?f_ri=995270"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_3118163" data-work_id="3118163" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/3118163/Cross_Modal_Dynamic_Capture_Congruency_Effects_in_the_Perception_of_Motion_Across_Sensory_Modalities">Cross-Modal Dynamic Capture: Congruency Effects in the Perception of Motion Across Sensory Modalities</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/3118163" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a 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Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=995270","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="499" href="https://www.academia.edu/Documents/in/Acoustics">Acoustics</a>, <script data-card-contents-for-ri="499" type="text/json">{"id":499,"name":"Acoustics","url":"https://www.academia.edu/Documents/in/Acoustics?f_ri=995270","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="3031" href="https://www.academia.edu/Documents/in/Auditory_Perception">Auditory Perception</a><script data-card-contents-for-ri="3031" type="text/json">{"id":3031,"name":"Auditory Perception","url":"https://www.academia.edu/Documents/in/Auditory_Perception?f_ri=995270","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=3118163]'), work: {"id":3118163,"title":"Cross-Modal Dynamic Capture: Congruency Effects in the Perception of Motion Across Sensory Modalities","created_at":"2013-03-26T08:35:48.436-07:00","url":"https://www.academia.edu/3118163/Cross_Modal_Dynamic_Capture_Congruency_Effects_in_the_Perception_of_Motion_Across_Sensory_Modalities?f_ri=995270","dom_id":"work_3118163","summary":null,"downloadable_attachments":[{"id":50452833,"asset_id":3118163,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":100012,"first_name":"Charles","last_name":"Spence","domain_name":"oxford","page_name":"CharlesSpence","display_name":"Charles Spence","profile_url":"https://oxford.academia.edu/CharlesSpence?f_ri=995270","photo":"https://0.academia-photos.com/100012/27574/25394/s65_charles.spence.jpg"}],"research_interests":[{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology?f_ri=995270","nofollow":false},{"id":237,"name":"Cognitive 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Motion","url":"https://www.academia.edu/Documents/in/Apparent_Motion?f_ri=995270"},{"id":198527,"name":"Motion","url":"https://www.academia.edu/Documents/in/Motion?f_ri=995270"},{"id":329844,"name":"Experimental","url":"https://www.academia.edu/Documents/in/Experimental?f_ri=995270"},{"id":995270,"name":"Visual Motion","url":"https://www.academia.edu/Documents/in/Visual_Motion?f_ri=995270"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_3264185" data-work_id="3264185" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/3264185/Orthogonal_Wall_Correction_for_Visual_Motion_Estimation">Orthogonal Wall Correction for Visual Motion Estimation</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/3264185" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="028eb6830dae11a683e91db55661fed9" rel="nofollow" data-download="{"attachment_id":50385337,"asset_id":3264185,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50385337/download_file?st=MTczMjQxMzAzNyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" 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Robots","url":"https://www.academia.edu/Documents/in/Mobile_Robots?f_ri=995270","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="182415" href="https://www.academia.edu/Documents/in/Error_Correction">Error Correction</a><script data-card-contents-for-ri="182415" type="text/json">{"id":182415,"name":"Error Correction","url":"https://www.academia.edu/Documents/in/Error_Correction?f_ri=995270","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=3264185]'), work: {"id":3264185,"title":"Orthogonal Wall Correction for Visual Motion 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u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/6655797/Designing_caption_production_rules_based_on_face_text_and_motion_detection">Designing caption production rules based on face, text, and motion detection</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Producing off-line captions for the deaf and hearing impaired people is a labor-intensive task that can require up to 18 hours of production per hour of film. Captions are placed manually close to the region of interest but it must avoid... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_6655797" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Producing off-line captions for the deaf and hearing impaired people is a labor-intensive task that can require up to 18 hours of production per hour of film. Captions are placed manually close to the region of interest but it must avoid masking human faces, texts or any moving objects that might be relevant to the story flow. Our goal is to use image processing techniques to reduce the off-line caption production process by automatically placing the captions on the proper consecutive frames. We implemented a computer-assisted captioning software tool which integrates detection of faces, texts and visual motion regions. The near frontal faces are detected using a cascade of weak classifier and tracked through a particle filter. Then, frames are scanned to perform text spotting and build a region map suitable for text recognition. Finally, motion mapping is based on the Lukas-Kanade optical flow algorithm and provides MPEG-7 motion descriptors. The combined detected items are then fed to a rule-based algorithm to determine the best captions localization for the related sequences of frames. This paper focuses on the defined rules to assist the human captioners and the results of a user evaluation for this approach.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/6655797" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="aaeeaa4c2ae89f8dff88ed73852c45d6" rel="nofollow" data-download="{"attachment_id":48765040,"asset_id":6655797,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/48765040/download_file?st=MTczMjQxMzAzNyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="10803736" href="https://crim.academia.edu/claudechapdelaine">claude chapdelaine</a><script data-card-contents-for-user="10803736" type="text/json">{"id":10803736,"first_name":"claude","last_name":"chapdelaine","domain_name":"crim","page_name":"claudechapdelaine","display_name":"claude chapdelaine","profile_url":"https://crim.academia.edu/claudechapdelaine?f_ri=995270","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_6655797 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="6655797"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 6655797, container: ".js-paper-rank-work_6655797", }); 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$(".js-view-count[data-work-id=6655797]").text(description); $(".js-view-count-work_6655797").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_6655797").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="6655797"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="1185" href="https://www.academia.edu/Documents/in/Image_Processing">Image Processing</a>, <script data-card-contents-for-ri="1185" type="text/json">{"id":1185,"name":"Image Processing","url":"https://www.academia.edu/Documents/in/Image_Processing?f_ri=995270","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1755" href="https://www.academia.edu/Documents/in/Eye_tracking">Eye tracking</a>, <script data-card-contents-for-ri="1755" type="text/json">{"id":1755,"name":"Eye tracking","url":"https://www.academia.edu/Documents/in/Eye_tracking?f_ri=995270","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="59378" href="https://www.academia.edu/Documents/in/Optical_Flow">Optical Flow</a>, <script data-card-contents-for-ri="59378" type="text/json">{"id":59378,"name":"Optical Flow","url":"https://www.academia.edu/Documents/in/Optical_Flow?f_ri=995270","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="92331" href="https://www.academia.edu/Documents/in/Particle_filters">Particle filters</a><script data-card-contents-for-ri="92331" type="text/json">{"id":92331,"name":"Particle filters","url":"https://www.academia.edu/Documents/in/Particle_filters?f_ri=995270","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=6655797]'), work: {"id":6655797,"title":"Designing caption production rules based on face, text, and motion detection","created_at":"2014-04-04T05:20:19.968-07:00","url":"https://www.academia.edu/6655797/Designing_caption_production_rules_based_on_face_text_and_motion_detection?f_ri=995270","dom_id":"work_6655797","summary":"Producing off-line captions for the deaf and hearing impaired people is a labor-intensive task that can require up to 18 hours of production per hour of film. Captions are placed manually close to the region of interest but it must avoid masking human faces, texts or any moving objects that might be relevant to the story flow. Our goal is to use image processing techniques to reduce the off-line caption production process by automatically placing the captions on the proper consecutive frames. We implemented a computer-assisted captioning software tool which integrates detection of faces, texts and visual motion regions. The near frontal faces are detected using a cascade of weak classifier and tracked through a particle filter. Then, frames are scanned to perform text spotting and build a region map suitable for text recognition. Finally, motion mapping is based on the Lukas-Kanade optical flow algorithm and provides MPEG-7 motion descriptors. The combined detected items are then fed to a rule-based algorithm to determine the best captions localization for the related sequences of frames. This paper focuses on the defined rules to assist the human captioners and the results of a user evaluation for this approach.","downloadable_attachments":[{"id":48765040,"asset_id":6655797,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":10803736,"first_name":"claude","last_name":"chapdelaine","domain_name":"crim","page_name":"claudechapdelaine","display_name":"claude chapdelaine","profile_url":"https://crim.academia.edu/claudechapdelaine?f_ri=995270","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1185,"name":"Image Processing","url":"https://www.academia.edu/Documents/in/Image_Processing?f_ri=995270","nofollow":false},{"id":1755,"name":"Eye tracking","url":"https://www.academia.edu/Documents/in/Eye_tracking?f_ri=995270","nofollow":false},{"id":59378,"name":"Optical Flow","url":"https://www.academia.edu/Documents/in/Optical_Flow?f_ri=995270","nofollow":false},{"id":92331,"name":"Particle filters","url":"https://www.academia.edu/Documents/in/Particle_filters?f_ri=995270","nofollow":false},{"id":93036,"name":"Motion Detection","url":"https://www.academia.edu/Documents/in/Motion_Detection?f_ri=995270"},{"id":111949,"name":"User based evaluation","url":"https://www.academia.edu/Documents/in/User_based_evaluation?f_ri=995270"},{"id":126237,"name":"Video Object","url":"https://www.academia.edu/Documents/in/Video_Object?f_ri=995270"},{"id":261121,"name":"Particle Filter","url":"https://www.academia.edu/Documents/in/Particle_Filter?f_ri=995270"},{"id":385144,"name":"Production Process","url":"https://www.academia.edu/Documents/in/Production_Process?f_ri=995270"},{"id":437126,"name":"Hearing Impaired","url":"https://www.academia.edu/Documents/in/Hearing_Impaired?f_ri=995270"},{"id":592995,"name":"Moving Object Recognition","url":"https://www.academia.edu/Documents/in/Moving_Object_Recognition?f_ri=995270"},{"id":786185,"name":"Software Tool","url":"https://www.academia.edu/Documents/in/Software_Tool?f_ri=995270"},{"id":862335,"name":"Production Rule","url":"https://www.academia.edu/Documents/in/Production_Rule?f_ri=995270"},{"id":995270,"name":"Visual Motion","url":"https://www.academia.edu/Documents/in/Visual_Motion?f_ri=995270"},{"id":1216932,"name":"Rule Based","url":"https://www.academia.edu/Documents/in/Rule_Based?f_ri=995270"},{"id":1372278,"name":"Region of Interest","url":"https://www.academia.edu/Documents/in/Region_of_Interest?f_ri=995270"},{"id":1561715,"name":"Image processing techniques","url":"https://www.academia.edu/Documents/in/Image_processing_techniques?f_ri=995270"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_10499650" data-work_id="10499650" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a 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Lobe","url":"https://www.academia.edu/Documents/in/Frontal_Lobe?f_ri=995270"},{"id":1394092,"name":"Beta Rhythm","url":"https://www.academia.edu/Documents/in/Beta_Rhythm?f_ri=995270"},{"id":1478742,"name":"Delta Rhythm","url":"https://www.academia.edu/Documents/in/Delta_Rhythm?f_ri=995270"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_13863793" data-work_id="13863793" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/13863793/Behavior_classification_by_eigendecomposition_of_periodic_motions">Behavior classification by eigendecomposition of periodic motions</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We show how periodic motions can be represented by a small number of eigenshapes that capture the whole dynamic mechanism of the motion. Spectral decomposition of a silhouette of a moving object serves as a basis for behavior... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_13863793" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We show how periodic motions can be represented by a small number of eigenshapes that capture the whole dynamic mechanism of the motion. Spectral decomposition of a silhouette of a moving object serves as a basis for behavior classification by principle component analysis. The boundary contour of the walking dog, for example, is first computed efficiently and accurately. After normalization, the implicit representation of a sequence of silhouette contours given by their corresponding binary images, is used for generating eigenshapes for the given motion. Singular value decomposition produces these eigenshapes that are then used to analyze the sequence. We show examples of object as well as behavior classification based on the eigendecomposition of the binary silhouette sequence.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/13863793" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e7f2405085c2f1b11229930be80998fa" rel="nofollow" data-download="{"attachment_id":44865653,"asset_id":13863793,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/44865653/download_file?st=MTczMjQxMzAzNyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32951870" href="https://google.academia.edu/RomanGoldenberg">Roman Goldenberg</a><script data-card-contents-for-user="32951870" type="text/json">{"id":32951870,"first_name":"Roman","last_name":"Goldenberg","domain_name":"google","page_name":"RomanGoldenberg","display_name":"Roman Goldenberg","profile_url":"https://google.academia.edu/RomanGoldenberg?f_ri=995270","photo":"https://0.academia-photos.com/32951870/67906287/56279962/s65_roman.goldenberg.png"}</script></span></span></li><li class="js-paper-rank-work_13863793 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="13863793"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 13863793, container: ".js-paper-rank-work_13863793", }); 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Spectral decomposition of a silhouette of a moving object serves as a basis for behavior classification by principle component analysis. The boundary contour of the walking dog, for example, is first computed efficiently and accurately. After normalization, the implicit representation of a sequence of silhouette contours given by their corresponding binary images, is used for generating eigenshapes for the given motion. Singular value decomposition produces these eigenshapes that are then used to analyze the sequence. We show examples of object as well as behavior classification based on the eigendecomposition of the binary silhouette sequence.","downloadable_attachments":[{"id":44865653,"asset_id":13863793,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32951870,"first_name":"Roman","last_name":"Goldenberg","domain_name":"google","page_name":"RomanGoldenberg","display_name":"Roman Goldenberg","profile_url":"https://google.academia.edu/RomanGoldenberg?f_ri=995270","photo":"https://0.academia-photos.com/32951870/67906287/56279962/s65_roman.goldenberg.png"}],"research_interests":[{"id":5109,"name":"Pattern Recognition","url":"https://www.academia.edu/Documents/in/Pattern_Recognition?f_ri=995270","nofollow":false},{"id":36449,"name":"Activity Recognition","url":"https://www.academia.edu/Documents/in/Activity_Recognition?f_ri=995270","nofollow":false},{"id":42835,"name":"Video 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The possibility of discovering laws relating human experience to activity in neural tissue seems closer with the continuing development of neuroimaging... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_50333559" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">One of the ultimate goals of neuroscience is to map the mind onto the brain. The possibility of discovering laws relating human experience to activity in neural tissue seems closer with the continuing development of neuroimaging techniques. 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The possibility of discovering laws relating human experience to activity in neural tissue seems closer with the continuing development of neuroimaging techniques. While few of these laws have been ...","downloadable_attachments":[{"id":68359892,"asset_id":50333559,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":24493290,"first_name":"Stephen","last_name":"Engel","domain_name":"independent","page_name":"StephenEngel","display_name":"Stephen Engel","profile_url":"https://independent.academia.edu/StephenEngel?f_ri=995270","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1026,"name":"Psychophysiology","url":"https://www.academia.edu/Documents/in/Psychophysiology?f_ri=995270","nofollow":false},{"id":5359,"name":"Visual perception","url":"https://www.academia.edu/Documents/in/Visual_perception?f_ri=995270","nofollow":false},{"id":6200,"name":"Magnetic Resonance Imaging","url":"https://www.academia.edu/Documents/in/Magnetic_Resonance_Imaging?f_ri=995270","nofollow":false},{"id":47884,"name":"Biological Sciences","url":"https://www.academia.edu/Documents/in/Biological_Sciences?f_ri=995270","nofollow":false},{"id":61474,"name":"Brain","url":"https://www.academia.edu/Documents/in/Brain?f_ri=995270"},{"id":64568,"name":"Humans","url":"https://www.academia.edu/Documents/in/Humans?f_ri=995270"},{"id":100946,"name":"Functional Imaging","url":"https://www.academia.edu/Documents/in/Functional_Imaging?f_ri=995270"},{"id":298502,"name":"Area Mt","url":"https://www.academia.edu/Documents/in/Area_Mt?f_ri=995270"},{"id":995270,"name":"Visual Motion","url":"https://www.academia.edu/Documents/in/Visual_Motion?f_ri=995270"},{"id":2922956,"name":"Psychology and Cognitive Sciences","url":"https://www.academia.edu/Documents/in/Psychology_and_Cognitive_Sciences?f_ri=995270"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences?f_ri=995270"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_31501604" data-work_id="31501604" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/31501604/Rotational_coherent_dot_movement_normalizes_spatial_disorientation_of_the_subjective_visual_vertical_in_patients_with_rightsided_stroke">Rotational coherent dot movement normalizes spatial disorientation of the subjective visual vertical in patients with rightsided stroke</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Studies in healthy individuals indicate a significant influence of rotating visual motion on judgments of the subjective visual vertical (SVV). Moreover, sensory stimulation manoeuvres like horizontal coherent dot movement significantly... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_31501604" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Studies in healthy individuals indicate a significant influence of rotating visual motion on judgments of the subjective visual vertical (SVV). Moreover, sensory stimulation manoeuvres like horizontal coherent dot movement significantly modulate horizontal spatial deficits in patients with rightsided stroke. Here, we investigated whether rotational coherent dot movement (RCDM) modulates spatial orientation deficits of the SVV in the roll plane in right hemispheric stroke. We tested the perceptual judgment of the SVV in 20 patients with right-hemispheric, first ever stroke (10 of them with a disorder of the SVV and 10 without a disorder), and 10 healthy, age-matched subjects under three experimental conditions: (1) with a static background of small white dots, (2) with slow clockwise or (3) counterclockwise circular RCDM of these background stimuli. In the baseline condition with static background, the impaired patient group showed a counterclockwise tilt of the SVV. Clockwise RCDM normalized this deficit completely, while with counterclockwise RCDM a slight aggravation was observed. Similar but quantitatively much smaller effects were obtained in the SVV-unimpaired patients and the healthy individuals. These results demonstrate a strong modulatory effect of RCDM on the SVV in patients with a tilt of the SVV due to right-sided stroke. RCDM thus appears to influence higher spatial representations devoted to visuospatial perception of the SVV. Possible mechanisms as well as clinical implications for therapy of visuospatial disorientation (self-orientation in space) after stroke are discussed.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/31501604" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2fec315e6897c0110f5e749a48c3f156" rel="nofollow" data-download="{"attachment_id":51849073,"asset_id":31501604,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/51849073/download_file?st=MTczMjQxMzAzNyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33773027" href="https://uni-saarland.academia.edu/GeorgKerkhoff">Georg Kerkhoff</a><script data-card-contents-for-user="33773027" type="text/json">{"id":33773027,"first_name":"Georg","last_name":"Kerkhoff","domain_name":"uni-saarland","page_name":"GeorgKerkhoff","display_name":"Georg Kerkhoff","profile_url":"https://uni-saarland.academia.edu/GeorgKerkhoff?f_ri=995270","photo":"https://0.academia-photos.com/33773027/10798795/12053079/s65_georg.kerkhoff.jpg"}</script></span></span></li><li class="js-paper-rank-work_31501604 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="31501604"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 31501604, container: ".js-paper-rank-work_31501604", }); 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Moreover, sensory stimulation manoeuvres like horizontal coherent dot movement significantly modulate horizontal spatial deficits in patients with rightsided stroke. Here, we investigated whether rotational coherent dot movement (RCDM) modulates spatial orientation deficits of the SVV in the roll plane in right hemispheric stroke. We tested the perceptual judgment of the SVV in 20 patients with right-hemispheric, first ever stroke (10 of them with a disorder of the SVV and 10 without a disorder), and 10 healthy, age-matched subjects under three experimental conditions: (1) with a static background of small white dots, (2) with slow clockwise or (3) counterclockwise circular RCDM of these background stimuli. In the baseline condition with static background, the impaired patient group showed a counterclockwise tilt of the SVV. Clockwise RCDM normalized this deficit completely, while with counterclockwise RCDM a slight aggravation was observed. Similar but quantitatively much smaller effects were obtained in the SVV-unimpaired patients and the healthy individuals. These results demonstrate a strong modulatory effect of RCDM on the SVV in patients with a tilt of the SVV due to right-sided stroke. RCDM thus appears to influence higher spatial representations devoted to visuospatial perception of the SVV. Possible mechanisms as well as clinical implications for therapy of visuospatial disorientation (self-orientation in space) after stroke are discussed.","downloadable_attachments":[{"id":51849073,"asset_id":31501604,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":33773027,"first_name":"Georg","last_name":"Kerkhoff","domain_name":"uni-saarland","page_name":"GeorgKerkhoff","display_name":"Georg Kerkhoff","profile_url":"https://uni-saarland.academia.edu/GeorgKerkhoff?f_ri=995270","photo":"https://0.academia-photos.com/33773027/10798795/12053079/s65_georg.kerkhoff.jpg"}],"research_interests":[{"id":8942,"name":"Treatment","url":"https://www.academia.edu/Documents/in/Treatment?f_ri=995270","nofollow":false},{"id":369895,"name":"Spatial Orientation","url":"https://www.academia.edu/Documents/in/Spatial_Orientation?f_ri=995270","nofollow":false},{"id":995270,"name":"Visual Motion","url":"https://www.academia.edu/Documents/in/Visual_Motion?f_ri=995270","nofollow":false},{"id":1431361,"name":"Brain Damage","url":"https://www.academia.edu/Documents/in/Brain_Damage?f_ri=995270","nofollow":false},{"id":2641668,"name":"visual vertical","url":"https://www.academia.edu/Documents/in/visual_vertical?f_ri=995270"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_3544087" data-work_id="3544087" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/3544087/Polysynaptic_inputs_to_vestibular_efferent_neurons_as_revealed_by_viral_transneuronal_tracing">Polysynaptic inputs to vestibular efferent neurons as revealed by viral transneuronal tracing</a></div></div><div class="u-pb4x u-mt3x"></div><ul 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