Effective Visual Working Memory Capacity: An Emergent Effect from the Neural Dynamics in an Attractor Network
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{"title"=>"Effective Visual Working Memory Capacity: An Emergent Effect from the Neural Dynamics in an Attractor Network", "type"=>"journal", "authors"=>[{"first_name"=>"Laura", "last_name"=>"Dempere-Marco", "scopus_author_id"=>"6506864955"}, {"first_name"=>"David P.", "last_name"=>"Melcher", "scopus_author_id"=>"55044707800"}, {"first_name"=>"Gustavo", "last_name"=>"Deco", "scopus_author_id"=>"7006674531"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84865611253", "pui"=>"365542833", "doi"=>"10.1371/journal.pone.0042719", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "sgr"=>"84865611253", "pmid"=>"22952608"}, "id"=>"9fb01fc9-1fec-380d-80c7-43872244298d", "abstract"=>"The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1) the presence of a visually salient item reduces the number of items that can be held in working memory, and 2) visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items. Our model suggests that working memory capacity is determined by two fundamental processes: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (eWMC) in contrast to the maximal upper capacity limit only reached under ideal conditions.", "link"=>"http://www.mendeley.com/research/effective-visual-working-memory-capacity-emergent-effect-neural-dynamics-attractor-network", "reader_count"=>62, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Librarian"=>2, "Researcher"=>13, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>3, "Student > Master"=>4, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>4, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Librarian"=>2, "Researcher"=>13, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>3, "Student > Master"=>4, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>4, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Nursing and Health Professions"=>1, "Mathematics"=>2, "Agricultural and Biological Sciences"=>13, "Medicine and Dentistry"=>4, "Design"=>1, "Neuroscience"=>4, "Arts and Humanities"=>1, "Physics and Astronomy"=>1, "Psychology"=>23, "Social Sciences"=>3, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Design"=>{"Design"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Neuroscience"=>{"Neuroscience"=>4}, "Social Sciences"=>{"Social Sciences"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>23}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>13}, "Computer Science"=>{"Computer Science"=>4}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>5}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Netherlands"=>2, "Argentina"=>1, "United States"=>2, "Japan"=>1, "United Kingdom"=>2, "France"=>1, "Portugal"=>1, "Switzerland"=>2, "Germany"=>2, "Spain"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/585639"], "description"=>"<p>Illustration of three possible neuronal behaviours observed when 3 pools are simultaneously stimulated. The stimulation period is represented by means of a thick back line. Noise introduces a stochastic component which is translated into a distribution of possible neuronal behaviours. <b>A</b> Trials in which no saliency effects are present and the three pools selective to the visual items in the display (pool 1, pool 3 and pool 5) receive an intensity  = 80 Hz, and <b>B</b> trials in which one of the three items in the visual display is salient and the pool selective to such item (pool 1) receives an external current  = 120 Hz instead of  = 80 Hz. The response of the pool selective to the salient stimulus always reaches an elevated firing rate which, in turn, recruits inhibition and may prevent other pools from reaching such elevated firing rates, and thus, reduces the <i>e</i>WMC.</p>", "links"=>[], "tags"=>["predictions", "firing"], "article_id"=>256140, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g014", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_predictions_of_firing_rates_/256140", "title"=>"Model predictions of firing rates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:42:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/585750"], "description"=>"<p>Parameters of the integrare-and-fire simulations.</p>", "links"=>[], "tags"=>["integrare-and-fire"], "article_id"=>256250, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.t001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameters_of_the_integrare_and_fire_simulations_/256250", "title"=>"Parameters of the integrare-and-fire simulations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-29 01:44:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/584805"], "description"=>"<p>The population of excitatory neurons is subdivided in non-overlapping populations selective to 8 different stimuli. Black and blue arrows within pyramidal cells: NMDA and AMPA-mediated recurrent excitatory connections. Black arrows from other areas: AMPA-mediated external excitatory connections. Red circle-headed arrows: GABA-mediated inhibitory connections. There are three possible synaptic strengths for recurrent excitatory connections: potentiated (by a relative factor , black arrows), depressed (by a relative factor , light blue arrows), and unchanged (baseline level , dark blue arrows). The weight denotes the strength of inhibitory-to-excitatory and inhibitory-to-inhibitory connections. The dots stand for the missing , …, populations and their corresponding connections.</p>", "links"=>[], "tags"=>["cortical"], "article_id"=>255303, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g001", "stats"=>{"downloads"=>1, "page_views"=>37, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Architecture_of_the_cortical_network_model_/255303", "title"=>"Architecture of the cortical network model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:28:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/585516"], "description"=>"<p>Proportion of correct responses for trials in which: <b>A</b> the bottom-up saliency of one of the items in the display was defined by manipulating the visual contrast, and <b>B</b> the top-down saliency of one of the items in the display resulted from an item appearing at a task-relevant location. Performance results for those trials in which the test stimulus is the salient item are distinguished from those in which the test stimulus is a non-salient item (denoted as “other” in the figure) to assess the influence of saliency on behavioural performance. Error bars show one standard error of the mean. Adapted with permission from Melcher and Piazza <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone.0042719-Melcher2\" target=\"_blank\">[19]</a>.</p>", "links"=>[], "tags"=>["saliency", "behavioural", "performace", "wm"], "article_id"=>256008, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g011", "stats"=>{"downloads"=>4, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Influence_of_saliency_on_behavioural_performace_in_a_visual_WM_task_/256008", "title"=>"Influence of saliency on behavioural performace in a visual WM task.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:40:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/585104"], "description"=>"<p>Results obtained from the network model with the parameters shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone-0042719-t001\" target=\"_blank\">Table 1</a>. The stimulation period during which the external stimulation is administered to the network to emulate the presence of 4 items in the visual display is  = 500 ms and is depicted by a black segment. <b>A</b> 4 pools (pool 1, pool 3, pool 5 and pool 7) receive an external stimulation  = 60 Hz and <b>B</b> the pool selective to the salient item (pool 1) receives an external stimulation  = 100 Hz while the remaining three stimulated pools (pool 3, pool 5 and pool 7) receive only  = 60 Hz. As a consequence of the biased competition in the visual saliency condition, the pool selective to the salient item quickly reaches a state of high firing rate during the stimulation period while preventing others from accessing such a state. This leads to fewer items being appropriately encoded into the visual WM system and, thus, would reduce performance in those trials in which the test item is different from the salient one.</p>", "links"=>[], "tags"=>["firing"], "article_id"=>255604, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_of_firing_rates_/255604", "title"=>"Prediction of firing rates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:33:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/585557"], "description"=>"<p>Proportion of correct responses for trials in which the bottom-up visual saliency of one item in the display was manipulated at different levels of visual contrast (also size in the case of the triangles). The number of items (set size) was held constant at three. Performance results for those trials in which the test stimulus is the salient item are distinguished from those in which the test stimulus is a non-salient item (denoted as “other” in the figure) to assess the influence of saliency level on behavioural performance. Error bars show one standard error of the mean. Adapted with permission from Melcher and Piazza <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone.0042719-Melcher2\" target=\"_blank\">[19]</a>.</p>", "links"=>[], "tags"=>["bottom-up", "saliency", "wm"], "article_id"=>256052, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g012", "stats"=>{"downloads"=>3, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Influence_of_bottom_up_saliency_level_on_performance_in_a_visual_WM_task_/256052", "title"=>"Influence of bottom-up saliency level on performance in a visual WM task.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:40:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/585166"], "description"=>"<p>Histograms illustrating the percentage of trials in which multiple items show high activity during the last 300 ms of the delay period for different stimulation periods: <b>A </b> = 200 ms, <b>B </b> = 500 ms, and <b>C </b> = 1000 ms. The set size of the memory set is 4 in these experiments. Four pools receive an external stimulation  = 60 Hz in the no-saliency condition whereas the pool selective to the salient item receives an external stimulation  = 100 Hz (while the remaining three stimulated pools receive only  = 60 Hz) in the saliency condition. The network parameters employed in these simulations are those shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone-0042719-t001\" target=\"_blank\">Table 1</a>. The results illustrate that <i>e</i>WMC increases when the stimulation period is increased and is reduced in the presence of saliency.</p>", "links"=>[], "tags"=>["items", "maintained", "wm", "stimulation"], "article_id"=>255663, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g006", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_number_of_items_maintained_in_visual_WM_as_a_function_of_the_stimulation_period_/255663", "title"=>"Distribution of number of items maintained in visual WM as a function of the stimulation period.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:34:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/306778"], "description"=>"<div><p>The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1) the presence of a visually salient item reduces the number of items that can be held in working memory, and 2) visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items.</p> <p>Our model suggests that working memory capacity is determined by two fundamental processes: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (<em>e</em>WMC) in contrast to the maximal upper capacity limit only reached under ideal conditions.</p> </div>", "links"=>[], "tags"=>["emergent", "neural", "attractor"], "article_id"=>120527, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719", "stats"=>{"downloads"=>7, "page_views"=>62, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Effective_Visual_Working_Memory_Capacity_An_Emergent_Effect_from_the_Neural_Dynamics_in_an_Attractor_Network/120527", "title"=>"Effective Visual Working Memory Capacity: An Emergent Effect from the Neural Dynamics in an Attractor Network", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-29 00:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/585350"], "description"=>"<p>Results from the simulations of the full spiking model with 2500 neurons in four different trials in the baseline condition with no visual saliency and four pools (pool 1, pool 3, pool 5 and pool 7) receiving an external stimulation  = 60 Hz. The results illustrate how smaller networks present larger finite size noise, which may lead to spontaneous memory losses throughout the delay period, and thus, reduce the <i>e</i>WMC of the system.</p>", "links"=>[], "tags"=>["rates", "smaller"], "article_id"=>255851, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g008", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Firing_rates_predicted_by_the_model_for_smaller_network_sizes_/255851", "title"=>"Firing rates predicted by the model for smaller network sizes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:37:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/584925"], "description"=>"<p>Model-based prediction of performance derived from computational simulations of a change detection task with selective neural assemblies ( axis) simultaneously stimulated. Performance is calculated by assuming that an item is held in visual WM when its associated selective pool shows a mean persistent activity 20 Hz during the last 300 ms of the delay period. selective pools are stimulated at different amplitude levels  = 40 Hz, 60 Hz, 80 Hz, and 100 Hz. <b>A</b> Performance calculated as (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone.0042719.e303\" target=\"_blank\">Eq. 7</a>), and <b>B</b> performance calculated as (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone.0042719.e305\" target=\"_blank\">Eq. 8</a>). For both proposed performance estimates, performance decreases for larger set sizes but improves for larger stimulation amplitudes up to a value ( = 80 Hz) beyond which performance seems to converge.</p>", "links"=>[], "tags"=>["levels"], "article_id"=>255423, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g003", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_based_prediction_of_performance_for_different_levels_of_external_stimulation_/255423", "title"=>"Model-based prediction of performance for different levels of external stimulation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:30:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/585013"], "description"=>"<p>Model-based prediction of performance derived from computational simulations of a change detection task with selective neural assemblies ( axis) simultaneously stimulated. Performance is calculated by assuming that an item is held in visual WM when its associated selective pool shows a mean persistent activity 20 Hz during the last 300 ms of the delay period. selective pools are stimulated at  = 60 Hz and the remaining pool receives a higher stimulation  = 60 Hz (no saliency), 70 Hz, 80 Hz, 100 Hz, 150 Hz, and 200 Hz. <b>A</b> Performance calculated as (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone.0042719.e303\" target=\"_blank\">Eq. 7</a>), and <b>B</b> performance calculated as (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone.0042719.e305\" target=\"_blank\">Eq. 8</a>). For both proposed performance estimates, performance decreases for larger set sizes and for larger saliency levels.</p>", "links"=>[], "tags"=>["levels"], "article_id"=>255513, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_based_prediction_of_performance_for_different_levels_of_saliency_/255513", "title"=>"Model-based prediction of performance for different levels of saliency.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:31:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/585592"], "description"=>"<p><b>A</b> Performance predicted by the model in a change detection task akin to the experiments in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone.0042719-Melcher2\" target=\"_blank\">[19]</a> (also see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone-0042719-g011\" target=\"_blank\">Fig. 11</a>) as a function of set size. The pool selective to the salient stimulus receives an external stimulation  = 120 Hz whereas the pools that are not selective to the salient stimulus but are stimulated receive an external stimulation  = 80 Hz during the 200 ms stimulation period. <b>B</b> Performance predicted by the model in a change detection task akin to the experiments in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone.0042719-Melcher2\" target=\"_blank\">[19]</a> (also see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone-0042719-g012\" target=\"_blank\">Fig. 12</a>) as a function of the saliency level. In the model, visual saliency is introduced by means of an upregulation of the neuronal responses to the item. To this end, different levels of additional external stimulation are considered:  = 80, 90, 100, 120, 140 and 160 Hz for set size 3. In both cases, the results are assessed separately for the trials in which the target item corresponds to the salient item and those which do not coincide with the salient item (i.e. other). The network parameters employed in these simulations are those shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone-0042719-t001\" target=\"_blank\">Table 1</a>. The results suggest a good qualitative conformance with the experimental results shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone-0042719-g011\" target=\"_blank\">Fig. 11</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone-0042719-g012\" target=\"_blank\">Fig. 12</a>. It is worth noting that no specific tuning of the network parameters has been sought to reproduce such results. The same parameters used in the previous studies, which reproduce the main results from the available literature, have also been used in this case.</p>", "links"=>[], "tags"=>["saliency", "wm"], "article_id"=>256088, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g013", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_prediction_of_saliency_influence_on_proportion_correct_in_a_visual_WM_task_/256088", "title"=>"Model prediction of saliency influence on proportion correct in a visual WM task.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:41:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/585418"], "description"=>"<p><b>.</b> Histograms indicating the percentage of trials in which multiple items show high activity during the last 300 ms of the delay period for different stimulation periods:  = 200 ms, 500 ms and 1000 ms, and different levels of additional external stimulation ( = 40, 60, and 80 Hz) received by the pools selective to the items in the visual display. <b>A</b> Four pools receive an external stimulation and <b>B</b> the pool selective to the salient item receives an external stimulation  = 100 Hz while the remaining three stimulated pools receive only . The network parameters employed in these simulations are those shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone-0042719-t001\" target=\"_blank\">Table 1</a>. The results show that increasing the administered external stimulation to all pools leads to an improvement in performace since the number of items held in visual WM increases overall. It is worth noting that the magnitude of the saliency effect is reduced for larger values of because the difference is smaller. The general trend observed in previous studies is, however, maintained in that the presence of a visually salient item in a display tends to reduce the number of items that are held in visual WM.</p>", "links"=>[], "tags"=>["items", "maintained", "wm"], "article_id"=>255916, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g009", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_number_of_items_maintained_in_visual_WM_as_a_function_of_the_additional_external_stimulation_/255916", "title"=>"Distribution of number of items maintained in visual WM as a function of the additional external stimulation", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:38:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/585714"], "description"=>"<p>The stimulation amplitude at baseline level is a limiting factor of <i>e</i>WMC. Not only a sufficiently large value is necessary to reach the full WM capacity of the network but also the absolute difference between and has different effects depending on the baseline level . <b>A</b> Performance predicted by the model in a change detection task akin to the experiments in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone.0042719-Melcher2\" target=\"_blank\">[19]</a> as a function of the saliency level. In the model, visual saliency is introduced by means of an upregulation of the neuronal responses to the item. To this end, different levels of additional external stimulation are considered:  = 60, 70, 80, 100, 120 and 140 Hz for set size 3. <b>B</b> Performance predicted by the model in a change detection task akin to the experiments in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone.0042719-Melcher2\" target=\"_blank\">[19]</a> as a function of set size. The pool selective to the salient stimulus receives an external stimulation  = 100 Hz whereas the pools that are not selective to the salient stimulus but are stimulated receive an external stimulation  = 60 Hz during the 200 ms stimulation period. This corresponds, in fact, to a particular working point of the graph in <b>A</b> for a varying set size. In this case, the salient item is always maintained in working memory throughout the delay period but random performance is obtained for non-salient items, thus suggesting a winner salient item in a winner-take-all network. In both plots, the results are assessed separately for the trials in which the target item corresponds to the salient item and those which do not coincide with the salient item (i.e. other). The network parameters employed in these simulations are those shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone-0042719-t001\" target=\"_blank\">Table 1</a>.</p>", "links"=>[], "tags"=>["dependence", "stimulation", "amplitude", "baseline"], "article_id"=>256211, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g015", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_dependence_on_stimulation_amplitude_at_baseline_level_/256211", "title"=>"Performance dependence on stimulation amplitude at baseline level.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:43:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/584857"], "description"=>"<p>Mean field analysis of the model assessing the dependence of the network behaviour on the potentiated synaptic strength () and the inhibitory synaptic strength (), for different initial conditions. <b>A</b> The initial firing rate conditions for pools showing high firing rates are derived from a Gaussian distribution with mean  = 40 Hz and standard deviation  = 0.01 Hz. The firing rates determining the initial conditions of pools in spontaneous states are obtained from randomly sampling a Gaussian distribution with mean  = 3 Hz and standard deviation  = 0.01 Hz. The colour code indicates the number of pools which settle on stable states showing persistently high firing rates ( Hz) during the delay period when no further stimulation is provided. <b>B</b> Identical initial conditions as in <b>A</b> but one of the pools showing an initially high firing rate of 65 Hz. From left to right an increasing number of pools had high initial firing rates. Note that as a consequence of considering a hard boundary (i.e. Hz, used in subsequent studies) for values some apparent discontinuities may appear for increasing values, which in fact correspond to stable states with persistent firing rates just below the threshold. However, this does not occur in the region where our working point is located (, ).</p>", "links"=>[], "tags"=>["neuroscience"], "article_id"=>255361, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g002", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_field_analysis_of_the_model_/255361", "title"=>"Mean field analysis of the model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:29:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/585227"], "description"=>"<p>Histograms illustrating the percentage of trials in which multiple items show high activity during the last 300 ms of the delay period for different stimulation periods:  = 200 ms, 500 ms and 1000 ms and different network sizes  = 2500, 5000 and 10000 neurons. <b>A</b> Four pools receive an additional external stimulation  = 60 Hz, and <b>B</b> the pool selective to the salient item receives an external stimulation  = 100 Hz while the remaining three stimulated pools receive only  = 60 Hz. The network parameters employed in these simulations are those shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone-0042719-t001\" target=\"_blank\">Table 1</a>. The results illustrate that <i>e</i>WMC increases when the stimulation period is increased for intermediate (500 ms) and long (1000 ms) stimulation periods, and is reduced in the presence of saliency. This tendency is stronger for larger network sizes as a consequence of the reduced finite size noise. For short stimulation periods (200 ms), the competition processes in the saliency condition favours a winner-take-all type of behaviour for the salient item.</p>", "links"=>[], "tags"=>["items", "maintained", "wm"], "article_id"=>255730, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g007", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_number_of_items_maintained_in_WM_as_a_function_of_network_size_/255730", "title"=>"Distribution of number of items maintained in WM as a function of network size.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:35:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/585469"], "description"=>"<p>A stimulus set (“memory set”), consisting of 1 to 4 oriented Gabor stimuli, was shown for 200 ms in order to discourage subjects from making saccadic eye movements to scan the individual items. Trials were started by a button press and the first stimulus frame was only displayed after a variable delay time (500–700 ms). A fixation point was maintained at the center of the screen throughout each block of trials. The orientation of each Gabor stimulus in the memory set was one of eight possible orientations (10, 20, 30 or 40 degrees from the vertical). A blank delay of 1000 ms followed the display of the memory set. Then, one probe stimulus (“test stimulus”) was shown for 200 ms. The test stimulus was identical to the Gabor patch at the same location in the memory set on “same trials”, whereas its orientation was mirror-reversed across the vertical on “different trials”. In the baseline condition, the Gabor stimuli had identical contrast and size (30% of full contrast). Separate blocks of trials were run in which the saliency of one item was manipulated by either increasing its bottom-up or top-down saliency. In the bottom-up saliency manipulation, the visual contrast with the background and/or the size of the Gabor stimulus was increased. Top-down saliency was manipulated by adding a memory-guided saccade task. To this end, a red dot was presented, along with the fixation point, at the beginning of the trials and participants were instructed to memorise this location in order to make a saccade there once the central fixation point was removed. Adapted with permission from Melcher and Piazza <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042719#pone.0042719-Melcher2\" target=\"_blank\">[19]</a>.</p>", "links"=>[], "tags"=>["neuroscience"], "article_id"=>255967, "categories"=>["Neuroscience"], "users"=>["Laura Dempere-Marco", "David P. Melcher", "Gustavo Deco"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0042719.g010", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experimental_setup_task_timeline_/255967", "title"=>"Experimental setup: task timeline.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-29 01:39:27"}

PMC Usage Stats | Further Information

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