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• W2138128929 abstract "A central criticism of standard theoretical approaches to constructing stable, recurrent model networks is that the synaptic connection weights need to be finely-tuned. This criticism is severe because proposed rules for learning these weights have been shown to have various limitations to their biological plausibility. Hence it is unlikely that such rules are used to continuously fine-tune the network in vivo. We describe a learning rule that is able to tune synaptic weights in a biologically plausible manner. We demonstrate and test this rule in the context of the oculomotor integrator, showing that only known neural signals are needed to tune the weights. We demonstrate that the rule appropriately accounts for a wide variety of experimental results, and is robust under several kinds of perturbation. Furthermore, we show that the rule is able to achieve stability as good as or better than that provided by the linearly optimal weights often used in recurrent models of the integrator. Finally, we discuss how this rule can be generalized to tune a wide variety of recurrent attractor networks, such as those found in head direction and path integration systems, suggesting that it may be used to tune a wide variety of stable neural systems." @default.
• W2138128929 created "2016-06-24" @default.
• W2138128929 creator A5036100595 @default.
• W2138128929 creator A5055630059 @default.
• W2138128929 date "2011-09-27" @default.
• W2138128929 modified "2023-09-30" @default.
• W2138128929 title "Fine-Tuning and the Stability of Recurrent Neural Networks" @default.
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• W2138128929 doi "https://doi.org/10.1371/journal.pone.0022885" @default.
• W2138128929 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3181247" @default.
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• W2138128929 hasPublicationYear "2011" @default.
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