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Advances in Neural Network Modeling
, 1997
"... Neural networks have become standard tools in modeling and classification. In this paper we discuss some advancements in controling the model complexity in neural network modeling, and give some practical advise to users of these techniques. ..."
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Cited by 2 (2 self)
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Neural networks have become standard tools in modeling and classification. In this paper we discuss some advancements in controling the model complexity in neural network modeling, and give some practical advise to users of these techniques.
The graph neural network model
 IEEE Transactions on Neural Networks
, 2009
"... The graph neural network model Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural netw ..."
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Cited by 11 (4 self)
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The graph neural network model Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural
Neural Network Models for Prediction
"... Abstract Neural networks are used to predict the drape coemcient (DC) and circularity (CIR) of many different kinds of fabrics. The neural network models used were the Multilayer Perceptron using Backpropagation (BP) and the Radial Basis Function (RBF) neural network. The BP method was found to be ..."
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Abstract Neural networks are used to predict the drape coemcient (DC) and circularity (CIR) of many different kinds of fabrics. The neural network models used were the Multilayer Perceptron using Backpropagation (BP) and the Radial Basis Function (RBF) neural network. The BP method was found
Neural network models of schizophrenia
 Neuroscientist
, 2001
"... There is considerable neurobiological evidence suggesting that schizophrenia is associated with reduced corticocortical connectivity. The authors describe two neural network computer simulations that explore functional consequences of these abnormalities. The first utilized an “attractor ” neural ne ..."
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Cited by 13 (0 self)
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only when input information was ambiguous and provide models for delusions and cognitive disorganization. A second neural network simulation examined effects of corticocortical pruning in a speech perception network. Excessive pruning caused the network to produce percepts spontaneously, that is
Prediction and Classification with Neural Network Models
 Sociological Methods and Research
, 1996
"... This paper compares neural network models with the standard logit and probit models, the most widely used choice/classification models in current empirical research, and explores the application of neural network models in analyzing political choice/classification problems. Political relationships a ..."
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Cited by 6 (1 self)
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This paper compares neural network models with the standard logit and probit models, the most widely used choice/classification models in current empirical research, and explores the application of neural network models in analyzing political choice/classification problems. Political relationships
Predicting the secondary structure of globular proteins using neural networks models
 J. Molecular Biology
, 1988
"... We present a new method for predicting the secondary structure of globular proteins based on nonlinear neural network models. Network models learn from existing protein structures how to predict the secondary structure of local sequences of amino acids. The average success rate of our method on a t ..."
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Cited by 270 (2 self)
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We present a new method for predicting the secondary structure of globular proteins based on nonlinear neural network models. Network models learn from existing protein structures how to predict the secondary structure of local sequences of amino acids. The average success rate of our method on a
A Neural Network Model of Causality
 IEEE TRANSACTIONS ON NEURAL NETWORKS
, 1994
"... This paper proposes a model for commonsense causal reasoning, based on the basic idea of neural networks. After an analysis of the advantages and limitations of existing accounts of causality, a fuzzy logic based formalism FEL is proposed that takes into account the inexactness and the cumulative ev ..."
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Cited by 9 (0 self)
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This paper proposes a model for commonsense causal reasoning, based on the basic idea of neural networks. After an analysis of the advantages and limitations of existing accounts of causality, a fuzzy logic based formalism FEL is proposed that takes into account the inexactness and the cumulative
Networks of Spiking Neurons: The Third Generation of Neural Network Models
, 1996
"... The computational power of formal models for networks of spiking neurons is compared with that of other neural network models based on McCulloch Pitts neurons (i.e. threshold gates) respectively sigmoidal gates. In particular it is shown that networks of spiking neurons are computationally more powe ..."
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Cited by 192 (14 self)
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The computational power of formal models for networks of spiking neurons is compared with that of other neural network models based on McCulloch Pitts neurons (i.e. threshold gates) respectively sigmoidal gates. In particular it is shown that networks of spiking neurons are computationally more
Adaptive Regularization in Neural Network Modeling
, 1997
"... . In this paper we address the important problem of optimizing regularization parameters in neural network modeling. The suggested optimization scheme is an extended version of the recently presented algorithm [24]. The idea is to minimize an empirical estimate  like the crossvalidation estimate ..."
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Cited by 21 (2 self)
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. In this paper we address the important problem of optimizing regularization parameters in neural network modeling. The suggested optimization scheme is an extended version of the recently presented algorithm [24]. The idea is to minimize an empirical estimate  like the crossvalidation estimate
Propagation of excitation in neural network models
 Network
, 1993
"... Abshct. We shldy the propagation of waves of excitation in neural network models. Thmugh analytic calculation and computer simulation, we determine how the pmpagation velocity depends w the range and strength of synaptic interadions, the firing threshold and on transmission delays. For the models co ..."
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Cited by 9 (0 self)
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Abshct. We shldy the propagation of waves of excitation in neural network models. Thmugh analytic calculation and computer simulation, we determine how the pmpagation velocity depends w the range and strength of synaptic interadions, the firing threshold and on transmission delays. For the models
Results 1  10
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