Learning Performance evaluated by classification accuracy of the original stimulus classes based on the output encoding of the neural network models.
<p>Left: No regularization, Right: With activity regularization. Dotted line: p < 0.05. Solid line: p < 0.0001.</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852015126898540544 |
|---|---|
| author | Dirk Gütlin (22566778) |
| author2 | Ryszard Auksztulewicz (4640515) |
| author2_role | author |
| author_facet | Dirk Gütlin (22566778) Ryszard Auksztulewicz (4640515) |
| author_role | author |
| dc.creator.none.fl_str_mv | Dirk Gütlin (22566778) Ryszard Auksztulewicz (4640515) |
| dc.date.none.fl_str_mv | 2025-11-05T18:26:33Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcsy.0000076.g006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Learning_Performance_evaluated_by_classification_accuracy_of_the_original_stimulus_classes_based_on_the_output_encoding_of_the_neural_network_models_/30544054 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biophysics Neuroscience Physiology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified without evaluating whether like effects across like behaviors better including mismatch responses biological neural networks compared two pc advancing brain modelling inspired training objectives like responses training algorithms neural learning work contributes underlying mechanisms semantic information saving principles results show promising foundation pc framework neuroscientific theory key signatures inspired algorithms important mechanism gain control findings indicate dnn ). contrastive approach accurately reflect |
| dc.title.none.fl_str_mv | Learning Performance evaluated by classification accuracy of the original stimulus classes based on the output encoding of the neural network models. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Left: No regularization, Right: With activity regularization. Dotted line: p < 0.05. Solid line: p < 0.0001.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_bd55daa2ee6a666f1017c7bf4b53439b |
| identifier_str_mv | 10.1371/journal.pcsy.0000076.g006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30544054 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Learning Performance evaluated by classification accuracy of the original stimulus classes based on the output encoding of the neural network models.Dirk Gütlin (22566778)Ryszard Auksztulewicz (4640515)BiophysicsNeurosciencePhysiologyScience PolicyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedwithout evaluating whetherlike effects acrosslike behaviors betterincluding mismatch responsesbiological neural networkscompared two pcadvancing brain modellinginspired training objectiveslike responsestraining algorithmsneural learningwork contributesunderlying mechanismssemantic informationsaving principlesresults showpromising foundationpc frameworkneuroscientific theorykey signaturesinspired algorithmsimportant mechanismgain controlfindings indicatednn ).contrastive approachaccurately reflect<p>Left: No regularization, Right: With activity regularization. Dotted line: p < 0.05. Solid line: p < 0.0001.</p>2025-11-05T18:26:33ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcsy.0000076.g006https://figshare.com/articles/figure/Learning_Performance_evaluated_by_classification_accuracy_of_the_original_stimulus_classes_based_on_the_output_encoding_of_the_neural_network_models_/30544054CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/305440542025-11-05T18:26:33Z |
| spellingShingle | Learning Performance evaluated by classification accuracy of the original stimulus classes based on the output encoding of the neural network models. Dirk Gütlin (22566778) Biophysics Neuroscience Physiology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified without evaluating whether like effects across like behaviors better including mismatch responses biological neural networks compared two pc advancing brain modelling inspired training objectives like responses training algorithms neural learning work contributes underlying mechanisms semantic information saving principles results show promising foundation pc framework neuroscientific theory key signatures inspired algorithms important mechanism gain control findings indicate dnn ). contrastive approach accurately reflect |
| status_str | publishedVersion |
| title | Learning Performance evaluated by classification accuracy of the original stimulus classes based on the output encoding of the neural network models. |
| title_full | Learning Performance evaluated by classification accuracy of the original stimulus classes based on the output encoding of the neural network models. |
| title_fullStr | Learning Performance evaluated by classification accuracy of the original stimulus classes based on the output encoding of the neural network models. |
| title_full_unstemmed | Learning Performance evaluated by classification accuracy of the original stimulus classes based on the output encoding of the neural network models. |
| title_short | Learning Performance evaluated by classification accuracy of the original stimulus classes based on the output encoding of the neural network models. |
| title_sort | Learning Performance evaluated by classification accuracy of the original stimulus classes based on the output encoding of the neural network models. |
| topic | Biophysics Neuroscience Physiology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified without evaluating whether like effects across like behaviors better including mismatch responses biological neural networks compared two pc advancing brain modelling inspired training objectives like responses training algorithms neural learning work contributes underlying mechanisms semantic information saving principles results show promising foundation pc framework neuroscientific theory key signatures inspired algorithms important mechanism gain control findings indicate dnn ). contrastive approach accurately reflect |