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Stabilizing patterns in time: Neural network approach
Recurrent and feedback networks are capable of holding dynamic memories. Nonetheless, training a network for that task is challenging. In order to do so, one should face non-linear propagation of errors in the system. Small deviations from the desired dynamics due to error or inherent noise might ha...
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| Veröffentlicht in: | PLoS Comput Biol |
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| Hauptverfasser: | , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
Public Library of Science
2017
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5741269/ https://ncbi.nlm.nih.gov/pubmed/29232710 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pcbi.1005861 |
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