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NN
2006
Springer
163views Neural Networks» more  NN 2006»
15 years 6 months ago
Machine learning approaches for estimation of prediction interval for the model output
A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the pr...
Durga L. Shrestha, Dimitri P. Solomatine
NN
2006
Springer
114views Neural Networks» more  NN 2006»
15 years 6 months ago
Modular learning models in forecasting natural phenomena
Modular model is a particular type of committee machine and is comprised of a set of specialized (local) models each of which is responsible for a particular region of the input s...
Dimitri P. Solomatine, Michael Baskara L. A. Siek
NN
2006
Springer
141views Neural Networks» more  NN 2006»
15 years 6 months ago
Encoding uncertainty in the hippocampus
The medial temporal lobe may play a critical role in binding successive events into memory while encoding contextual information in implicit and explicit memory tasks. Information...
Lee M. Harrison, Andrew Duggins, Karl J. Friston
NN
2006
Springer
234views Neural Networks» more  NN 2006»
15 years 6 months ago
Attention in natural scenes: Neurophysiological and computational bases
How does attention operate in natural scenes? We show that the receptive fields of inferior temporal cortex neurons that implement object representations become small and located ...
Edmund T. Rolls, Gustavo Deco
IJON
2007
85views more  IJON 2007»
15 years 6 months ago
Hierarchical dynamical models of motor function
Hierarchical models of motor function are described in which the motor system encodes a hierarchy of dynamical motor primitives. The models are based on continuous attractor neura...
Simon M. Stringer, Edmund T. Rolls