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• W1661597557 abstract "It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for two main reasons: nonlinear recurrent networks often exhibit chaotic behavior and most known learning rules do not work in robust fashion in recurrent networks. Here we address both these problems by demonstrating how random recurrent networks (RRN) that initially exhibit chaotic dynamics can be tuned through a supervised learning rule to generate locally stable neural patterns of activity that are both complex and robust to noise. The outcome is a novel neural network regime that exhibits both transiently stable and chaotic trajectories. We further show that the recurrent learning rule dramatically increases the ability of RRNs to generate complex spatiotemporal motor patterns, and accounts for recent experimental data showing a decrease in neural variability in response to stimulus onset." @default.
• W1661597557 created "2016-06-24" @default.
• W1661597557 creator A5004330476 @default.
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• W1661597557 date "2013-05-26" @default.
• W1661597557 modified "2023-10-14" @default.
• W1661597557 title "Robust timing and motor patterns by taming chaos in recurrent neural networks" @default.
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• W1661597557 doi "https://doi.org/10.1038/nn.3405" @default.
• W1661597557 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3753043" @default.
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