Parallel Fiber Coding in the Cerebellum for Life-Long Learning

被引：0
|
作者：
Olivier J.-M.D. Coenen
Michael P. Arnold
Terrence J. Sejnowski
Marwan A. Jabri
机构：
[1] Howard Hughes Medical Institute,Computational Neurobiology Laboratory
[2] Salk Institute,Department of Biology
[3] UCSD,Electrical and Computer Engineering
[4] Oregon Graduate Institute,undefined
来源：
Autonomous Robots | 2001年 / 11卷
关键词：
cerebellum; learning; plasticity; Bayesian; maximum likelihood; information theory; sparse; statistically independent; independent component analysis; ICA; coding; code; model; spiking; granule cell; mossy fiber; Golgi cell; glomerulus; parallel fiber; granular layer; robotics; smooth pursuit; saccade; motor control; destructive interference;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Continuous and real-time learning is a difficult problem in robotics. To learn efficiently, it is important to recognize the current situation and learn appropriately for that context. To be effective, this requires the integration of a large number of sensorimotor and cognitive signals. So far, few principles on how to perform this integration have been proposed. Another limitation is the difficulty to include the complete contextual information to avoid destructive interference while learning different tasks.
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页码：291 / 297
页数：6
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