Model and experiments.
<p>A. Structure of the dataset: binary vectors are organized along a temporal scale. Some of the vectors repeat after time steps, others are randomly generated. Repeated vectors are considered familiar and are labeled as 1, non-repeated vectors are non-familiar and labeled as 0. <b>B<...
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| _version_ | 1851484048990404608 |
|---|---|
| author | Viktoria Zemliak (19500335) |
| author2 | Gordon Pipa (153913) Pascal Nieters (14611325) |
| author2_role | author author |
| author_facet | Viktoria Zemliak (19500335) Gordon Pipa (153913) Pascal Nieters (14611325) |
| author_role | author |
| dc.creator.none.fl_str_mv | Viktoria Zemliak (19500335) Gordon Pipa (153913) Pascal Nieters (14611325) |
| dc.date.none.fl_str_mv | 2025-08-01T17:58:52Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013304.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Model_and_experiments_/29801797 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Neuroscience Biological Sciences not elsewhere classified Information Systems not elsewhere classified sparse coding observed previously encountered inputs network activity using lateral connectivity shaped connectivity structure supports approach outperforms lstm sparse input conditions recurrent connectivity without continual familiarity decoding recurrent connections input stimuli decoding strategy unsupervised spike spike trains spike synchrony spike count naturally encoded dependent plasticity |
| dc.title.none.fl_str_mv | Model and experiments. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>A. Structure of the dataset: binary vectors are organized along a temporal scale. Some of the vectors repeat after time steps, others are randomly generated. Repeated vectors are considered familiar and are labeled as 1, non-repeated vectors are non-familiar and labeled as 0. <b>B</b>. Three degrees of input sparseness are used in the experiment: 0.6, 0.8, 0.9. <b>C</b>. Inputs with correlation level modeled as template similarity, i.e., the fraction of externally stimulated neurons matching those in the same template activation pattern: 0.0 (main experiments), 0.2, 0.4, 0.6, 0.8 (see Methods Correlated inputs). <b>D</b>. The model architecture: each Izhikevich neuron [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013304#pcbi.1013304.ref031" target="_blank">31</a>] has a one-to-one connection to the spiking input. Izhikevich neurons are laterally connected to one another. Lateral connections undergo unidirectional STDP or anti-STDP.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_a61d90b3be9f3f1c477d00c5cf92bb7e |
| identifier_str_mv | 10.1371/journal.pcbi.1013304.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29801797 |
| 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 and experiments.Viktoria Zemliak (19500335)Gordon Pipa (153913)Pascal Nieters (14611325)NeuroscienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsparse coding observedpreviously encountered inputsnetwork activity usinglateral connectivity shapedconnectivity structure supportsapproach outperforms lstmsparse input conditionsrecurrent connectivity withoutcontinual familiarity decodingrecurrent connectionsinput stimulidecoding strategyunsupervised spikespike trainsspike synchronyspike countnaturally encodeddependent plasticity<p>A. Structure of the dataset: binary vectors are organized along a temporal scale. Some of the vectors repeat after time steps, others are randomly generated. Repeated vectors are considered familiar and are labeled as 1, non-repeated vectors are non-familiar and labeled as 0. <b>B</b>. Three degrees of input sparseness are used in the experiment: 0.6, 0.8, 0.9. <b>C</b>. Inputs with correlation level modeled as template similarity, i.e., the fraction of externally stimulated neurons matching those in the same template activation pattern: 0.0 (main experiments), 0.2, 0.4, 0.6, 0.8 (see Methods Correlated inputs). <b>D</b>. The model architecture: each Izhikevich neuron [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013304#pcbi.1013304.ref031" target="_blank">31</a>] has a one-to-one connection to the spiking input. Izhikevich neurons are laterally connected to one another. Lateral connections undergo unidirectional STDP or anti-STDP.</p>2025-08-01T17:58:52ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013304.g001https://figshare.com/articles/figure/Model_and_experiments_/29801797CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/298017972025-08-01T17:58:52Z |
| spellingShingle | Model and experiments. Viktoria Zemliak (19500335) Neuroscience Biological Sciences not elsewhere classified Information Systems not elsewhere classified sparse coding observed previously encountered inputs network activity using lateral connectivity shaped connectivity structure supports approach outperforms lstm sparse input conditions recurrent connectivity without continual familiarity decoding recurrent connections input stimuli decoding strategy unsupervised spike spike trains spike synchrony spike count naturally encoded dependent plasticity |
| status_str | publishedVersion |
| title | Model and experiments. |
| title_full | Model and experiments. |
| title_fullStr | Model and experiments. |
| title_full_unstemmed | Model and experiments. |
| title_short | Model and experiments. |
| title_sort | Model and experiments. |
| topic | Neuroscience Biological Sciences not elsewhere classified Information Systems not elsewhere classified sparse coding observed previously encountered inputs network activity using lateral connectivity shaped connectivity structure supports approach outperforms lstm sparse input conditions recurrent connectivity without continual familiarity decoding recurrent connections input stimuli decoding strategy unsupervised spike spike trains spike synchrony spike count naturally encoded dependent plasticity |