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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC5741269?pdf=render |