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30 October 2009 Modeling of cortical signals using echo state networks
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Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74961M (2009)
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Diverse modeling frameworks have been utilized with the ultimate goal of translating brain cortical signals into prediction of visible behavior. The inputs to these models are usually multidimensional neural recordings collected from relevant regions of a monkey's brain while the outputs are the associated behavior which is typically the 2-D or 3-D hand position of a primate. Here our task is to set up a proper model in order to figure out the move trajectories by input the neural signals which are simultaneously collected in the experiment. In this paper, we propose to use Echo State Networks (ESN) to map the neural firing activities into hand positions. ESN is a newly developed recurrent neural network(RNN) model. Besides its dynamic property and short term memory just as other recurrent neural networks have, it has a special echo state property which endows it with the ability to model nonlinear dynamic systems powerfully. What distinguished it from transitional recurrent neural networks most significantly is its special learning method. In this paper we train this net with a refined version of its typical training method and get a better model.
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Hanying Zhou, Yongji Wang, and Jiangshuai Huang "Modeling of cortical signals using echo state networks", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961M (30 October 2009);

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