Variations of parameters in the “position” and “shape” stimulus datasets.
<p>(A). The center-dot distance <i>d</i> of the “position” dataset was varied and the classification accuracy rates of the network with 0% and 30% LRCs were measured. Note that the performance of the network without LRCs (LRC 0%) decreased significantly as <i>d</i> was...
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2023
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| _version_ | 1855384466001756160 |
|---|---|
| author | Seungdae Baek (14048664) |
| author2 | Youngjin Park (195938) Se-Bum Paik (242170) |
| author2_role | author author |
| author_facet | Seungdae Baek (14048664) Youngjin Park (195938) Se-Bum Paik (242170) |
| author_role | author |
| dc.creator.none.fl_str_mv | Seungdae Baek (14048664) Youngjin Park (195938) Se-Bum Paik (242170) |
| dc.date.none.fl_str_mv | 2023-08-04T17:21:24Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1011343.s002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Variations_of_parameters_in_the_position_and_shape_stimulus_datasets_/23870939 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Neuroscience Ecology Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> long using computational simulations results provide insight range horizontal connections dense local connections conspicuous anatomical structures theoretical model demonstrates sparse lrcs added balance functional performance world network depends primary visual cortex model network world network model simulation world coefficient visual processing visual information visual cortex network exceeds network appeared various sizes various conditions strongly correlated specific wiring specific existence key components fully understood efficient integration detailed functions cortices validates cortical circuits certain threshold biological strategy animal data &# 8220 |
| dc.title.none.fl_str_mv | Variations of parameters in the “position” and “shape” stimulus datasets. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>(A). The center-dot distance <i>d</i> of the “position” dataset was varied and the classification accuracy rates of the network with 0% and 30% LRCs were measured. Note that the performance of the network without LRCs (LRC 0%) decreased significantly as <i>d</i> was increased, whereas that of the network with LRCs (30%) was fairly consistent. (B). The classification performance for “position” stimulus increases and also becomes less vulnerable to variations of the stimulus condition as the LRC ratio increases. (C). The “shape” dataset consists of four numbers selected from 0 to 9. All possible combinations of digits (210 in total) were tested. (D). The network with LRCs (LRC 30%) showed lower performance than that of the network without LRCs (LRC 0%).</p> <p>(TIF)</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_2dfccd31345facf4dc2abfa33df2dee3 |
| identifier_str_mv | 10.1371/journal.pcbi.1011343.s002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/23870939 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Variations of parameters in the “position” and “shape” stimulus datasets.Seungdae Baek (14048664)Youngjin Park (195938)Se-Bum Paik (242170)NeuroscienceEcologyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedxlink "> longusing computational simulationsresults provide insightrange horizontal connectionsdense local connectionsconspicuous anatomical structurestheoretical model demonstratessparse lrcs addedbalance functional performanceworld network dependsprimary visual cortexmodel networkworld networkmodel simulationworld coefficientvisual processingvisual informationvisual cortexnetwork exceedsnetwork appearedvarious sizesvarious conditionsstrongly correlatedspecific wiringspecific existencekey componentsfully understoodefficient integrationdetailed functionscortices validatescortical circuitscertain thresholdbiological strategyanimal data&# 8220<p>(A). The center-dot distance <i>d</i> of the “position” dataset was varied and the classification accuracy rates of the network with 0% and 30% LRCs were measured. Note that the performance of the network without LRCs (LRC 0%) decreased significantly as <i>d</i> was increased, whereas that of the network with LRCs (30%) was fairly consistent. (B). The classification performance for “position” stimulus increases and also becomes less vulnerable to variations of the stimulus condition as the LRC ratio increases. (C). The “shape” dataset consists of four numbers selected from 0 to 9. All possible combinations of digits (210 in total) were tested. (D). The network with LRCs (LRC 30%) showed lower performance than that of the network without LRCs (LRC 0%).</p> <p>(TIF)</p>2023-08-04T17:21:24ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1011343.s002https://figshare.com/articles/figure/Variations_of_parameters_in_the_position_and_shape_stimulus_datasets_/23870939CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/238709392023-08-04T17:21:24Z |
| spellingShingle | Variations of parameters in the “position” and “shape” stimulus datasets. Seungdae Baek (14048664) Neuroscience Ecology Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> long using computational simulations results provide insight range horizontal connections dense local connections conspicuous anatomical structures theoretical model demonstrates sparse lrcs added balance functional performance world network depends primary visual cortex model network world network model simulation world coefficient visual processing visual information visual cortex network exceeds network appeared various sizes various conditions strongly correlated specific wiring specific existence key components fully understood efficient integration detailed functions cortices validates cortical circuits certain threshold biological strategy animal data &# 8220 |
| status_str | publishedVersion |
| title | Variations of parameters in the “position” and “shape” stimulus datasets. |
| title_full | Variations of parameters in the “position” and “shape” stimulus datasets. |
| title_fullStr | Variations of parameters in the “position” and “shape” stimulus datasets. |
| title_full_unstemmed | Variations of parameters in the “position” and “shape” stimulus datasets. |
| title_short | Variations of parameters in the “position” and “shape” stimulus datasets. |
| title_sort | Variations of parameters in the “position” and “shape” stimulus datasets. |
| topic | Neuroscience Ecology Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> long using computational simulations results provide insight range horizontal connections dense local connections conspicuous anatomical structures theoretical model demonstrates sparse lrcs added balance functional performance world network depends primary visual cortex model network world network model simulation world coefficient visual processing visual information visual cortex network exceeds network appeared various sizes various conditions strongly correlated specific wiring specific existence key components fully understood efficient integration detailed functions cortices validates cortical circuits certain threshold biological strategy animal data &# 8220 |