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NN
1998
Springer
102views Neural Networks» more  NN 1998»
15 years 4 months ago
A learning model for oscillatory networks
A learning model for coupled oscillators is proposed. The proposed learning rule takes a simple form by which the intrinsic frequencies of the component oscillators and the coupli...
Jun Nishii
AUTOMATICA
2006
90views more  AUTOMATICA 2006»
15 years 5 months ago
An ISS-modular approach for adaptive neural control of pure-feedback systems
Controlling non-affine non-linear systems is a challenging problem in control theory. In this paper, we consider adaptive neural control of a completely non-affine pure-feedback s...
Cong Wang, David J. Hill, S. S. Ge, Guanrong Chen
ICANN
2010
Springer
15 years 6 months ago
Recurrence Enhances the Spatial Encoding of Static Inputs in Reservoir Networks
We shed light on the key ingredients of reservoir computing and analyze the contribution of the network dynamics to the spatial encoding of inputs. Therefore, we introduce attracto...
Christian Emmerich, René Felix Reinhart, Jo...
136
Voted
NIPS
1998
15 years 6 months ago
Computational Differences between Asymmetrical and Symmetrical Networks
Symmetrically connected recurrent networks have recently been used as models of a host of neural computations. However, biological neural networks have asymmetrical connections, at...
Zhaoping Li, Peter Dayan
JMLR
2010
160views more  JMLR 2010»
14 years 11 months ago
Neural conditional random fields
We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Mar...
Trinh Minh Tri Do, Thierry Artières