Robust timing and motor patterns by taming chaos in recurrent neural networks

被引：0
|
作者：
Rodrigo Laje
Dean V Buonomano
机构：
[1] University of California,Department of Neurobiology
[2] University of California,Department of Psychology
[3] Brain Research Institute,undefined
[4] University of California,undefined
[5] Integrative Center for Learning and Memory,undefined
[6] University of California,undefined
[7] Present addresses: Departamento de Ciencia y Tecnología,undefined
[8] Universidad Nacional de Quilmes,undefined
[9] Bernal,undefined
[10] Argentina,undefined
[11] and Consejo Nacional de Investigaciones Científicas y Técnicas,undefined
[12] Buenos Aires,undefined
[13] Argentina.,undefined
来源：
Nature Neuroscience | 2013年 / 16卷
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中图分类号：
学科分类号：
摘要：
Here the authors describe a recurrent neural network model that tells time on the order of seconds and generates complex spatiotemporal motor patterns in the presence of high levels of noise. Robustness is achieved through the tuning of the recurrent connections, which produces stable patterns in the face of perturbations.
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页码：925 / 933
页数：8
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