Model structure of sparse/predictive coding (SPC).
<p>The first layer computes the prediction error between the input and reconstruction of the model, and then sends error signals to the second layer. The second layer takes input from the first layer and incorporates the response-regulating mechanism, . This two-layer network implements the dy...
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2025
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| _version_ | 1852019983696003072 |
|---|---|
| author | Yanbo Lian (10219563) |
| author2 | Anthony N. Burkitt (6732233) |
| author2_role | author |
| author_facet | Yanbo Lian (10219563) Anthony N. Burkitt (6732233) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yanbo Lian (10219563) Anthony N. Burkitt (6732233) |
| dc.date.none.fl_str_mv | 2025-05-27T18:23:09Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013059.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Model_structure_of_sparse_predictive_coding_SPC_/29160530 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Physiology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified substantial experimental evidence replicates different forms primary visual cortex learn simple cells still poorly understood implementing predictive coding regulates neural responses implements sparse coding learning framework based predictive coding neural responses sparse coding sparse responses predictive structure neural circuits well investigated unified framework study demonstrates relating sparse network structure many parts learning framework integrate input hebbian learning framework incorporates explicitly within divisive normalization described within contrast saturation biophysical properties |
| dc.title.none.fl_str_mv | Model structure of sparse/predictive coding (SPC). |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The first layer computes the prediction error between the input and reconstruction of the model, and then sends error signals to the second layer. The second layer takes input from the first layer and incorporates the response-regulating mechanism, . This two-layer network implements the dynamics described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013059#pcbi.1013059.e055" target="_blank">Eq 10</a>.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_e51250dd0ee5d45a6aeb8e2e997baa3a |
| identifier_str_mv | 10.1371/journal.pcbi.1013059.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29160530 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Model structure of sparse/predictive coding (SPC).Yanbo Lian (10219563)Anthony N. Burkitt (6732233)PhysiologyScience PolicyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsubstantial experimental evidencereplicates different formsprimary visual cortexlearn simple cellsstill poorly understoodimplementing predictive codingregulates neural responsesimplements sparse codinglearning framework basedpredictive codingneural responsessparse codingsparse responsespredictive structureneural circuitswell investigatedunified frameworkstudy demonstratesrelating sparsenetwork structuremany partslearning frameworkintegrate inputhebbian learningframework incorporatesexplicitly withindivisive normalizationdescribed withincontrast saturationbiophysical properties<p>The first layer computes the prediction error between the input and reconstruction of the model, and then sends error signals to the second layer. The second layer takes input from the first layer and incorporates the response-regulating mechanism, . This two-layer network implements the dynamics described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013059#pcbi.1013059.e055" target="_blank">Eq 10</a>.</p>2025-05-27T18:23:09ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013059.g002https://figshare.com/articles/figure/Model_structure_of_sparse_predictive_coding_SPC_/29160530CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291605302025-05-27T18:23:09Z |
| spellingShingle | Model structure of sparse/predictive coding (SPC). Yanbo Lian (10219563) Physiology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified substantial experimental evidence replicates different forms primary visual cortex learn simple cells still poorly understood implementing predictive coding regulates neural responses implements sparse coding learning framework based predictive coding neural responses sparse coding sparse responses predictive structure neural circuits well investigated unified framework study demonstrates relating sparse network structure many parts learning framework integrate input hebbian learning framework incorporates explicitly within divisive normalization described within contrast saturation biophysical properties |
| status_str | publishedVersion |
| title | Model structure of sparse/predictive coding (SPC). |
| title_full | Model structure of sparse/predictive coding (SPC). |
| title_fullStr | Model structure of sparse/predictive coding (SPC). |
| title_full_unstemmed | Model structure of sparse/predictive coding (SPC). |
| title_short | Model structure of sparse/predictive coding (SPC). |
| title_sort | Model structure of sparse/predictive coding (SPC). |
| topic | Physiology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified substantial experimental evidence replicates different forms primary visual cortex learn simple cells still poorly understood implementing predictive coding regulates neural responses implements sparse coding learning framework based predictive coding neural responses sparse coding sparse responses predictive structure neural circuits well investigated unified framework study demonstrates relating sparse network structure many parts learning framework integrate input hebbian learning framework incorporates explicitly within divisive normalization described within contrast saturation biophysical properties |