Reservoir Computing Trends

被引：279
作者：
Lukoševičius, Mantas [1 ]
Jaeger, Herbert [1 ]
Schrauwen, Benjamin [2 ]
机构：
[1] Jacobs University Bremen, Campus Ring 1, Bremen
[2] Ghent University, Sint Pietersnieuwstraat 41, Ghent
来源：
KI - Kunstliche Intelligenz | 2012年 / 26卷 / 04期
关键词：
Echo state network; Recurrent neural network; Reservoir computing;
D O I：
10.1007/s13218-012-0204-5
中图分类号：
学科分类号：
摘要：
Reservoir Computing (RC) is a paradigm of understanding and training Recurrent Neural Networks (RNNs) based on treating the recurrent part (the reservoir) differently than the readouts from it. It started ten years ago and is currently a prolific research area, giving important insights into RNNs, practical machine learning tools, as well as enabling computation with non-conventional hardware. Here we give a brief introduction into basic concepts, methods, insights, current developments, and highlight some applications of RC. © 2012, Springer-Verlag.
引用
收藏
页码：365 / 371
页数：6
相关论文
共 53 条
[1]  
Atiya A.F., Parlos A.G., New results on recurrent network training: unifying the algorithms and accelerating convergence, IEEE Trans Neural Netw, 11, 3, pp. 697-709, (2000)
[2]  
Bengio Y., Simard P., Frasconi P., Learning long-term dependencies with gradient descent is difficult, IEEE Trans Neural Netw, 5, 2, pp. 157-166, (1994)
[3]  
Bernacchia A., Seo H., Lee D., Wang X.J., A reservoir of time constants for memory traces in cortical neurons, Nat Neurosci, 14, 3, pp. 366-372, (2011)
[4]  
Buesing L., Bill J., Nessler B., Maass W., Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons, PLoS Comput Biol, 7, 11, (2011)
[5]  
Bunke H., Varga T., Off-line roman cursive handwriting recognition, Digital document processing, advances in pattern recognition, pp. 165-183, (2007)
[6]  
Buonomano D.V., Maass W., State-dependent computations: spatiotemporal processing in cortical networks, Nat Rev, Neurosci, 10, 2, pp. 113-125, (2009)
[7]  
Buteneers P., Verstraeten D., van Mierlo P., Wyckhuys T., Stroobandt D., Raedt R., Hallez H., Schrauwen B., Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing, Artif Intell Med, 53, 3, pp. 215-223, (2011)
[8]  
Dominey P.F., Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning, Biol Cybern, 73, pp. 265-274, (1995)
[9]  
Dominey P.F., From sensorimotor sequence to grammatical construction: evidence from simulation and neurophysiology, Adapt Behav, 13, 4, pp. 347-361, (2005)
[10]  
Dominey P.F., Ramus F., Neural network processing of natural language. I. Sensitivity to serial, temporal and abstract structure of language in the infant, Lang Cogn Processes, 15, 1, pp. 87-127, (2000)
123456