scholarly journals Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling

2007 ◽  
Vol 43 (11) ◽  
Author(s):  
Elena Toth ◽  
Armando Brath
Keyword(s):  
Neural Network ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Calibration Data ◽  
Streamflow Forecasting

scholarly journals Complementary learning systems within the hippocampus: A neural network modeling approach to reconciling episodic memory with statistical learning

2016 ◽  
Author(s):  
Anna C. Schapiro ◽  
Nicholas B. Turk-Browne ◽  
Matthew M. Botvinick ◽  
Kenneth A. Norman
Keyword(s):  
Neural Network ◽  
Statistical Learning ◽  
Entorhinal Cortex ◽  
Network Modeling ◽  
Learning Systems ◽  
Neural Network Modeling ◽  
Trade Off ◽  
Rapid Learning ◽  
Rapid Extraction

AbstractA growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway — the pathway connecting entorhinal cortex directly to region CA1 — was able to support statistical learning, while the trisynaptic pathway — connecting entorhinal cortex to CA1 through dentate gyrus and CA3 — learned only individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself.

scholarly journals Influence of Circulation Patterns on Temperature Behavior at the Regional Scale: A Case Study Investigated via Neural Network Modeling

2012 ◽  
Vol 25 (6) ◽  
pp. 2123-2128 ◽  
Author(s):  
Antonello Pasini ◽  
Rocco Langone
Keyword(s):  
Neural Network ◽  
Network Modeling ◽  
Regional Scale ◽  
Neural Network Modeling ◽  
Limited Region ◽  
Temperature Changes ◽  
The North ◽  
Circulation Patterns ◽  
The North Atlantic Oscillation

Abstract An analysis of the influence of circulation patterns on temperature changes in an extended Italian Alpine area has been performed by nonlinear methods and neural network modeling. This leads to the clarification of the roles of these patterns in the various seasons and permits the development of models that are able to reconstruct in a satisfactory manner the behavior of temperature anomalies in the second half of the twentieth century in this limited region. This nonlinear analysis shows that the role of the North Atlantic Oscillation (NAO) is probably overestimated, and that European blocking and the Scandinavian pattern should be considered as prime candidates as temperature drivers in this area.

Water quality parameter forecast based on grey neural network modeling

2009 ◽  
Vol 29 (6) ◽  
pp. 1529-1531 ◽  
Author(s):  
Wei-ren SHI ◽  
Yan-xia WANG ◽  
Yun-jian TANG ◽  
Min FAN
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Network Modeling ◽  
Quality Parameter ◽  
Water Quality Parameter ◽  
Neural Network Modeling

ε-Insensitive Criterion and Structure Risk Based Radius-basis-function Neural-network Modeling

2012 ◽  
Vol 34 (6) ◽  
pp. 1414-1419
Author(s):  
Qing-bing Sang ◽  
Zhao-hong Deng ◽  
Shi-tong Wang ◽  
Xiao-jun Wu
Keyword(s):  
Neural Network ◽  
Basis Function ◽  
Network Modeling ◽  
Neural Network Modeling
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