Modelling Adaptation to Directional Motion Using the Adelson-Bergen Energy Sensor
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{"title"=>"Modelling Adaptation to Directional Motion Using the Adelson-Bergen Energy Sensor", "type"=>"journal", "authors"=>[{"first_name"=>"Andrea", "last_name"=>"Pavan", "scopus_author_id"=>"57195729008"}, {"first_name"=>"Adriano", "last_name"=>"Contillo", "scopus_author_id"=>"37074435700"}, {"first_name"=>"George", "last_name"=>"Mather", "scopus_author_id"=>"7005494768"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84875031653", "pmid"=>"23555013", "scopus"=>"2-s2.0-84875031653", "pui"=>"368535284", "isbn"=>"1932-6203 (Electronic)\r1932-6203 (Linking)", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0059298"}, "id"=>"f7b1035f-f013-3d8e-9934-8ad0a4823790", "abstract"=>"The motion energy sensor has been shown to account for a wide range of physiological and psychophysical results in motion detection and discrimination studies. It has become established as the standard computational model for retinal movement sensing in the human visual system. Adaptation effects have been extensively studied in the psychophysical literature on motion perception, and play a crucial role in theoretical debates, but the current implementation of the energy sensor does not provide directly for modelling adaptation-induced changes in output. We describe an extension of the model to incorporate changes in output due to adaptation. The extended model first computes a space-time representation of the output to a given stimulus, and then a RC gain-control circuit (\"leaky integrator\") is applied to the time-dependent output. The output of the extended model shows effects which mirror those observed in psychophysical studies of motion adaptation: a decline in sensor output during stimulation, and changes in the relative of outputs of different sensors following this adaptation.", "link"=>"http://www.mendeley.com/research/modelling-adaptation-directional-motion-using-adelsonbergen-energy-sensor", "reader_count"=>19, "reader_count_by_academic_status"=>{"Researcher"=>3, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>2, "Other"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Researcher"=>3, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>2, "Other"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Agricultural and Biological Sciences"=>2, "Medicine and Dentistry"=>3, "Neuroscience"=>1, "Physics and Astronomy"=>1, "Psychology"=>11, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Neuroscience"=>{"Neuroscience"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>11}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}}, "reader_count_by_country"=>{"New Zealand"=>1, "United States"=>1, "United Kingdom"=>1, "France"=>1}, "group_count"=>0}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/1556663"], "description"=>"<p>MAE strength is shown as a function of the time (s) on the test phase at which the model output is evaluated (filled circles) <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059298#pone.0059298-Hershenson1\" target=\"_blank\">[14]</a>. Model fits are shown in separate panels for two adaptation durations: 120 s (left panel) and 150 s (right panel). The pair of <i>a</i> and <i>τ</i> that produced the best fit were <i>a</i>  =  0.911 and <i>τ</i>  =  95.60. The RMSE obtained for 120 s adaptation was 0.037, whereas for 150 s adaptation was 0.059.</p>", "links"=>[], "tags"=>["fitted", "psychophysical"], "article_id"=>1076308, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Andrea Pavan", "Adriano Contillo", "George Mather"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059298.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Extended_motion_energy_model_output_fitted_to_the_psychophysical_data_/1076308", "title"=>"Extended motion energy model output fitted to the psychophysical data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-15 10:49:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/1556660"], "description"=>"<p>The input stimulus consisted of a space-time representation of a squarewave adapting grating drifting leftward and with duration of 120 or 150 s (the image represents an adapting stimulus of 120 s). The adapting stimulus was immediately followed by a stationary test stimulus. The space-time presentation of the adapting pattern (i.e., tilted bars) appears quite coarse, but this is a merely graphic artifact caused by the necessity to resize an image with a vertical dimension much larger than the horizontal one. However, the input matrix is composed of smoothly leftward drifting black and white bars.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience"], "article_id"=>1076305, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Andrea Pavan", "Adriano Contillo", "George Mather"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059298.g003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Space_time_xt_representation_of_the_stimulus_/1076305", "title"=>"Space-time (xt) representation of the stimulus.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-15 10:49:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/1556661"], "description"=>"<p>The outputs are averaged across the spatial dimension. The dashed line indicates no motion. <i>t<sub>A</sub></i> indicates the offset of the adapting pattern and <i>T</i> the duration of the stimulus.</p>", "links"=>[], "tags"=>["outputs"], "article_id"=>1076306, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Andrea Pavan", "Adriano Contillo", "George Mather"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059298.g004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_between_the_outputs_of_the_standard_black_line_and_extended_gray_line_motion_energy_models_/1076306", "title"=>"Comparison between the outputs of the standard (black line) and extended (gray line) motion energy models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-15 10:49:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/1556658"], "description"=>"<p>The integrator stage is located after the squaring stage. See text for details.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience"], "article_id"=>1076303, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Andrea Pavan", "Adriano Contillo", "George Mather"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059298.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_extended_motion_energy_model_/1076303", "title"=>"The extended motion energy model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-15 10:49:28"}

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